Journal of the American Medical Informatics Association : JAMIA最新文献

{"title":"Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department","authors":"C. Redfield, A. Tlimat, Yoni Halpern, David W. Schoenfeld, Edward Ullman, D. Sontag, L. Nathanson, S. Horng","doi":"10.1093/jamia/ocz176","DOIUrl":"https://doi.org/10.1093/jamia/ocz176","url":null,"abstract":"OBJECTIVE\u0000Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR and ED record linkage have had limited success. In this study, we use supervised machine learning to derive and validate an automated record linkage algorithm between EMS ePCRs and ED records.\u0000\u0000\u0000MATERIALS AND METHODS\u0000All consecutive ePCRs from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCRs to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number, and date of birth were extracted. Data were randomly split into 80% training and 20% test datasets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5-fold cross-validation, using label k-fold, L2 regularization, and class reweighting.\u0000\u0000\u0000RESULTS\u0000A total of 14 032 ePCRs were included in the study. Interrater reliability between the primary and secondary reviewer had a kappa of 0.9. The algorithm had a sensitivity of 99.4%, a positive predictive value of 99.9%, and an area under the receiver-operating characteristic curve of 0.99 in both the training and test datasets. Date-of-birth match had the highest odds ratio of 16.9, followed by last name match (10.6). Social security number match had an odds ratio of 3.8.\u0000\u0000\u0000CONCLUSIONS\u0000We were able to successfully derive and validate a record linkage algorithm from a single EMS ePCR provider to our hospital EMR.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224129","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}

{"title":"Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review","authors":"Saba Akbar, E. Coiera, F. Magrabi","doi":"10.1093/jamia/ocz175","DOIUrl":"https://doi.org/10.1093/jamia/ocz175","url":null,"abstract":"Abstract Objective To summarize the research literature about safety concerns with consumer-facing health apps and their consequences. Materials and Methods We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into natural categories. The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. Results Of the 74 studies identified, the majority were reviews of a single or a group of similar apps (n = 66, 89%), nearly half related to disease management (n = 34, 46%). A total of 80 safety concerns were identified, 67 related to the quality of information presented including incorrect or incomplete information, variation in content, and incorrect or inappropriate response to consumer needs. The remaining 13 related to app functionality including gaps in features, lack of validation for user input, delayed processing, failure to respond to health dangers, and faulty alarms. Of the 52 reports of actual or potential consequences, 5 had potential for patient harm. We also identified 66 reports about gaps in app development, including the lack of expert involvement, poor evidence base, and poor validation. Conclusions Safety of apps is an emerging public health issue. The available evidence shows that apps pose clinical risks to consumers. Involvement of consumers, regulators, and healthcare professionals in development and testing can improve quality. Additionally, mandatory reporting of safety concerns is needed to improve outcomes.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"94 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126052903","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}

{"title":"Training medical students and residents in the use of electronic health records: a systematic review of the literature","authors":"A. Rajaram, Z. Hickey, N. Patel, J. Newbigging, B. Wolfrom","doi":"10.1093/jamia/ocz178","DOIUrl":"https://doi.org/10.1093/jamia/ocz178","url":null,"abstract":"OBJECTIVE\u0000Our objectives were to identify educational interventions designed to equip medical students or residents with knowledge or skills related to various uses of electronic health records (EHRs), summarize and synthesize the results of formal evaluations of these initiatives, and compare the aims of these initiatives with the prescribed EHR-specific competencies for undergraduate and postgraduate medical education.\u0000\u0000\u0000MATERIALS AND METHODS\u0000We conducted a systematic review of the literature following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) guidelines. We searched for English-language, peer-reviewed studies across 6 databases using a combination of Medical Subject Headings and keywords. We summarized the quantitative and qualitative results of included studies and rated studies according to the Best Evidence in Medical Education system.\u0000\u0000\u0000RESULTS\u0000Our search yielded 619 citations, of which 11 studies were included. Seven studies involved medical students, 3 studies involved residents, and 1 study involved both groups. All interventions used a practical component involving entering information into a simulated or prototypical EHR. None of the interventions involved extracting, aggregating, or visualizing clinical data for panels of patients or specific populations.\u0000\u0000\u0000DISCUSSION\u0000This review reveals few high-quality initiatives focused on training learners to engage with EHRs for both individual patient care and population health improvement. In comparing these interventions with the broad set of electronic records competencies expected of matriculating physicians, critical gaps in undergraduate and postgraduate medical education remain.\u0000\u0000\u0000CONCLUSIONS\u0000With the increasing adoption of EHRs and rise of competency-based medical education, educators should address the gaps in the training of future physicians to better prepare them to provide high quality care for their patients and communities.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"50 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120908917","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}

{"title":"Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis","authors":"Pavithra I. Dissanayake, Tiago K. Colicchio, J. Cimino","doi":"10.1093/jamia/ocz169","DOIUrl":"https://doi.org/10.1093/jamia/ocz169","url":null,"abstract":"Abstract Objective The study sought to describe the literature describing clinical reasoning ontology (CRO)–based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research. Methods MEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles. Results We included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment. Conclusion We identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians’ during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133909197","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}

{"title":"Ensuring electronic medical record simulation through better training, modeling, and evaluation","authors":"Ziqi Zhang, Chao Yan, Diego A. Mesa, Jimeng Sun, B. Malin","doi":"10.1093/jamia/ocz161","DOIUrl":"https://doi.org/10.1093/jamia/ocz161","url":null,"abstract":"OBJECTIVE\u0000Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In this article, we improve EMR simulation through a novel pipeline that (1) enhances the learning model, (2) incorporates evaluation criteria for data utility that informs learning, and (3) refines the training process.\u0000\u0000\u0000MATERIALS AND METHODS\u0000We propose a new electronic health record generator using a GAN with a Wasserstein divergence and layer normalization techniques. We designed 2 utility measures to characterize similarity in the structural properties of real and simulated EMRs in the original and latent space, respectively. We applied a filtering strategy to enhance GAN training for low-prevalence clinical concepts. We evaluated the new and existing GANs with utility and privacy measures (membership and disclosure attacks) using billing codes from over 1 million EMRs at Vanderbilt University Medical Center.\u0000\u0000\u0000RESULTS\u0000The proposed model outperformed the state-of-the-art approaches with significant improvement in retaining the nature of real records, including prediction performance and structural properties, without sacrificing privacy. Additionally, the filtering strategy achieved higher utility when the EMR training dataset was small.\u0000\u0000\u0000CONCLUSIONS\u0000These findings illustrate that EMR simulation through GANs can be substantially improved through more appropriate training, modeling, and evaluation criteria.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122648216","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}

{"title":"A step closer to nationwide electronic health record-based chronic disease surveillance: characterizing asthma prevalence and emergency department utilization from 100 million patient records through a novel multisite collaboration","authors":"Y. Tarabichi, Jake Goyden, Rujia Liu, S. Lewis, Joseph Sudano, D. Kaelber","doi":"10.1093/jamia/ocz172","DOIUrl":"https://doi.org/10.1093/jamia/ocz172","url":null,"abstract":"OBJECTIVE\u0000The study sought to assess the feasibility of nationwide chronic disease surveillance using data aggregated through a multisite collaboration of customers of the same electronic health record (EHR) platform across the United States.\u0000\u0000\u0000MATERIALS AND METHODS\u0000An independent confederation of customers of the same EHR platform proposed and guided the development of a program that leverages native EHR features to allow customers to securely contribute de-identified data regarding the prevalence of asthma and rate of asthma-associated emergency department visits to a vendor-managed repository. Data were stratified by state, age, sex, race, and ethnicity. Results were qualitatively compared with national survey-based estimates.\u0000\u0000\u0000RESULTS\u0000The program accumulated information from 100 million health records from over 130 healthcare systems in the United States over its first 14 months. All states were represented, with a median coverage of 22.88% of an estimated state's population (interquartile range, 12.05%-42.24%). The mean monthly prevalence of asthma was 5.27 ± 0.11%. The rate of asthma-associated emergency department visits was 1.39 ± 0.08%. Both measures mirrored national survey-based estimates.\u0000\u0000\u0000DISCUSSION\u0000By organizing the program around native features of a shared EHR platform, we were able to rapidly accumulate population level measures from a sizeable cohort of health records, with representation from every state. The resulting data allowed estimates of asthma prevalence that were comparable to data from traditional epidemiologic surveys at both geographic and demographic levels.\u0000\u0000\u0000CONCLUSIONS\u0000Our initiative demonstrates the potential of intravendor customer collaboration and highlights an organizational approach that complements other data aggregation efforts seeking to achieve nationwide EHR-based chronic disease surveillance.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132649951","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}

{"title":"The complementary nature of query-based and directed health information exchange in primary care practice","authors":"J. Vest, M. Unruh, L. Casalino, J. Shapiro","doi":"10.1093/jamia/ocz134","DOIUrl":"https://doi.org/10.1093/jamia/ocz134","url":null,"abstract":"OBJECTIVE\u0000Many policymakers and advocates assume that directed and query-based health information exchange (HIE) work together to meet organizations' interoperability needs, but this is not grounded in a substantial evidence base. This study sought to clarify the relationship between the usage of these 2 approaches to HIE.\u0000\u0000\u0000MATERIALS AND METHODS\u0000System user log files from a regional HIE organization and electronic health record system were combined to model the usage of HIE associated with a patient visit at 3 federally qualified health centers in New York. Regression models tested the hypothesis that directed HIE usage was associated with query-based usage and adjusted for factors reflective of the FITT (Fit between Individuals, Task & Technology) framework. Follow-up interviews with 8 key informants helped interpret findings.\u0000\u0000\u0000RESULTS\u0000Usage of query-based HIE occurred in 3.1% of encounters and directed HIE in 23.5%. Query-based usage was 0.6 percentage points higher when directed HIE provided imaging information, and 4.8 percentage points higher when directed HIE provided clinical documents. The probability of query-based HIE was lower for specialist visits, higher for postdischarge visits, and higher for encounters with nurse practitioners. Informants used query-based HIE after directed HIE to obtain additional information, support transitions of care, or in cases of abnormal results.\u0000\u0000\u0000DISCUSSION\u0000The complementary nature of directed and query-based HIE indicates that both HIE functionalities should be incorporated into EHR Certification Criteria.\u0000\u0000\u0000CONCLUSIONS\u0000Quantitative and qualitative findings suggest that directed and query-based HIE exist in a complementary manner in ambulatory care settings.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124752750","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}

{"title":"Consumer health information and question answering: helping consumers find answers to their health-related information needs","authors":"Dina Demner-Fushman, Yassine Mrabet, Asma Ben Abacha","doi":"10.1093/jamia/ocz152","DOIUrl":"https://doi.org/10.1093/jamia/ocz152","url":null,"abstract":"OBJECTIVE\u0000Consumers increasingly turn to the internet in search of health-related information; and they want their questions answered with short and precise passages, rather than needing to analyze lists of relevant documents returned by search engines and reading each document to find an answer. We aim to answer consumer health questions with information from reliable sources.\u0000\u0000\u0000MATERIALS AND METHODS\u0000We combine knowledge-based, traditional machine and deep learning approaches to understand consumers' questions and select the best answers from consumer-oriented sources. We evaluate the end-to-end system and its components on simple questions generated in a pilot development of MedlinePlus Alexa skill, as well as the short and long real-life questions submitted to the National Library of Medicine by consumers.\u0000\u0000\u0000RESULTS\u0000Our system achieves 78.7% mean average precision and 87.9% mean reciprocal rank on simple Alexa questions, and 44.5% mean average precision and 51.6% mean reciprocal rank on real-life questions submitted by National Library of Medicine consumers.\u0000\u0000\u0000DISCUSSION\u0000The ensemble of deep learning, domain knowledge, and traditional approaches recognizes question type and focus well in the simple questions, but it leaves room for improvement on the real-life consumers' questions. Information retrieval approaches alone are sufficient for finding answers to simple Alexa questions. Answering real-life questions, however, benefits from a combination of information retrieval and inference approaches.\u0000\u0000\u0000CONCLUSION\u0000A pilot practical implementation of research needed to help consumers find reliable answers to their health-related questions demonstrates that for most questions the reliable answers exist and can be found automatically with acceptable accuracy.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130128876","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}

{"title":"Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning","authors":"Long Chen, Yu Gu, Xin Ji, Zhiyong Sun, Haodan Li, Yuan Gao, Y. Huang","doi":"10.1093/jamia/ocz141","DOIUrl":"https://doi.org/10.1093/jamia/ocz141","url":null,"abstract":"OBJECTIVE\u0000Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction.\u0000\u0000\u0000MATERIALS AND METHODS\u0000The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks.\u0000\u0000\u0000RESULTS\u0000The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had <2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement.\u0000\u0000\u0000CONCLUSIONS\u0000We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126529484","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}

{"title":"Integrating pharmacogenomics into the electronic health record by implementing genomic indicators","authors":"P. Caraballo, Joseph A. Sutton, Jyothsna Giri, Jessica A. Wright, W. Nicholson, I. Kullo, M. Parkulo, S. Bielinski, A. Moyer","doi":"10.1093/jamia/ocz177","DOIUrl":"https://doi.org/10.1093/jamia/ocz177","url":null,"abstract":"Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"123 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133311518","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}