Journal of healthcare informatics research最新文献

{"title":"Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients.","authors":"Sungrim Moon, Sijia Liu, David Chen, Yanshan Wang, Douglas L Wood, Rajeev Chaudhry, Hongfang Liu, Paul Kingsbury","doi":"10.1007/s41666-019-00044-5","DOIUrl":"https://doi.org/10.1007/s41666-019-00044-5","url":null,"abstract":"<p><p>Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those common concepts, 73.97% were identified in initial consultant notes (ICNs) and 26.03% were captured over subsequent follow-up consultant notes (FCNs). These findings implied that information collected from the OMRs is potentially informative for patient care, but some valuable information (additionally identified in FCNs) collected from the OMRs is not fully used in an earlier stage of the care process. The concepts collected from the ICNs have the highest salience to Cardiovascular Medicine (0.112) compared to concepts in OMRs and concepts in FCNs. Additionally, unique concepts captured in ICNs (unseen in OMRs or FCNs) carried the most salient information (0.094), which demonstrated that ICNs provided the most informative concepts for the care of transferred patients.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"3 2","pages":"200-219"},"PeriodicalIF":0.0,"publicationDate":"2019-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.","authors":"Mehedi Hasan, April Idalski Carcone, Sylvie Naar, Susan Eggly, Gwen L Alexander, Kathryn E Brogan Hartlieb, Alexander Kotov","doi":"10.1007/s41666-018-0037-6","DOIUrl":"https://doi.org/10.1007/s41666-018-0037-6","url":null,"abstract":"<p><p>Motivational Interviewing (MI) is an evidence-based strategy for communicating with patients about behavior change. Although there is strong empirical evidence linking \"MI-consistent\" counselor behaviors and patient motivational statements (i.e., \"change talk\"), the specific counselor communication behaviors effective for eliciting patient change talk vary by treatment context and, thus, are a subject of ongoing research. An integral part of this research is the sequential analysis of pre-coded MI transcripts. In this paper, we evaluate the empirical effectiveness of the Hidden Markov Model, a probabilistic generative model for sequence data, for modeling sequences of behavior codes and closed frequent pattern mining, a method to identify frequently occurring sequential patterns of behavior codes in MI communication sequences to inform MI practice. We conducted experiments with 1,360 communication sequences from 37 transcribed audio recordings of weight loss counseling sessions with African-American adolescents with obesity and their caregivers. Transcripts had been previously annotated with patient-counselor behavior codes using a specialized codebook. Empirical results indicate that Hidden Markov Model and closed frequent pattern mining techniques can identify counselor communication strategies that are effective at eliciting patients' motivational statements to guide clinical practice.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"3 1","pages":"86-106"},"PeriodicalIF":4.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148764214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Happened to Me while I Was in the Hospital? Challenges and Opportunities for Generating Patient-Friendly Hospitalization Summaries.","authors":"Sabita Acharya, Andrew D Boyd, Richard Cameron, Karen Dunn Lopez, Pamela Martyn-Nemeth, Carolyn Dickens, Amer Ardati, Jose D Flores, Matt Baumann, Betty Welland, Barbara Di Eugenio","doi":"10.1007/s41666-018-0036-7","DOIUrl":"10.1007/s41666-018-0036-7","url":null,"abstract":"<p><p>Comprehending medical information is a challenging task, especially for people who have not received formal medical education. When patients are discharged from the hospital, they are provided with lengthy medical documents that contain intricate terminologies. Studies have shown that if people do not understand the content of their health documents, they will neither look for new information regarding their illness nor will they take actions to prevent or recover from their health issue. In this article, we highlight the need for generating personalized hospital-stay summaries and several research challenges associated with this task. The proposed directions are directly informed by our ongoing work in generating concise and comprehensible hospitalization summaries that are tailored to suit the patient's understanding of medical terminologies and level of engagement in improving their own health. Our preliminary evaluation shows that our summaries effectively present required medical concepts.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"3 1","pages":"107-123"},"PeriodicalIF":3.7,"publicationDate":"2018-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual Mobility and Uncertain Geographic Context: Real-time Versus Neighborhood Approximated Exposure to Retail Tobacco Outlets Across the US.","authors":"Thomas R Kirchner, Hong Gao, Daniel J Lewis, Andrew Anesetti-Rothermel, Heather A Carlos, Brian House","doi":"10.1007/s41666-018-0035-8","DOIUrl":"https://doi.org/10.1007/s41666-018-0035-8","url":null,"abstract":"<p><p>There is growing interest in the way exposure to neighborhood risk and protective factors affects the health of residents. Although multiple approaches have been reported, empirical methods for contrasting the spatial uncertainty of exposure estimates are not well established. The objective of this paper was to contrast real-time versus neighborhood approximated exposure to the landscape of tobacco outlets across the contiguous US. A nationwide density surface of tobacco retail outlet locations was generated using kernel density estimation (KDE). This surface was linked to participants' (<i>N</i> _<i>p</i>  = 363) inferred residential location, as well as to their real-time geographic locations, recorded every 10 min over 180 days. Real-time exposure was estimated as the hourly product of radius of gyration and average tobacco outlet density (<i>N</i> _hour = 304, 164 h). Ordinal logit modeling was used to assess the distribution of real-time exposure estimates as a function of each participant's residential exposure. Overall, 61.3% of real-time, hourly exposures were of relatively low intensity, and after controlling for temporal and seasonal variation, 72.8% of the variance among these low-level exposures was accounted for by residence in one of the two lowest residential exposure quintiles. Most moderate to high intensity exposures (38.7% of all real-time, hourly exposures) were no more likely to have been contributed by subjects from any single residential exposure cluster than another. Altogether, 55.2% of the variance in real-time exposures was <i>not</i> explained by participants' residential exposure cluster. Calculating hourly exposure estimates made it possible to directly contrast real-time observations with static residential exposure estimates. Results document the substantial degree that real-time exposures can be misclassified by residential approximations, especially in residential areas characterized by moderate to high retail density levels.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"3 1","pages":"70-85"},"PeriodicalIF":5.4,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-submodular Optimization (BSMO) for Detecting Drug-Drug Interactions (DDIs) from On-line Health Forums.","authors":"Yan Hu, Rui Wang, Feng Chen","doi":"10.1007/s41666-018-0032-y","DOIUrl":"https://doi.org/10.1007/s41666-018-0032-y","url":null,"abstract":"<p><p>Online health discussion forums as information exchange repository are used by different patient groups for sharing experience and seeking advice. Their accessibility is tremendously expanded in the last decade with the rapid growth of mobile internet. Among many popular topics, \"drug-drug interactions\" (DDIs) forum embeds a large number of DDIs hazards patient experienced however not published. In this paper, we intend to uncover the potential DDIs from the online forums and formulate the task as a sub-graph detection problem, such that co-mentioned drugs and symptoms are modeled as vertices, along with the occurrences are modeled as weighted edges. Therefore, a connected sub-graph consisting of both symptoms and drug vertices reveals DDIs occurrence. We then propose a novel bi-submodular function to characterize the likelihood of DDI occurrence within a connected sub-graph and apply an approximated algorithm to resolve the bi-submodular optimization (BSMO). The complexity of the algorithm is nearly linear. Our extensive experiments demonstrate the effectiveness and efficiency of the proposed approach.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"3 1","pages":"19-42"},"PeriodicalIF":5.4,"publicationDate":"2018-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The State of Data in Healthcare: Path Towards Standardization.","authors":"Keith Feldman, Reid A Johnson, Nitesh V Chawla","doi":"10.1007/s41666-018-0019-8","DOIUrl":"https://doi.org/10.1007/s41666-018-0019-8","url":null,"abstract":"<p><p>Coupled with the rise of data science and machine learning, the increasing availability of digitized health and wellness data has provided an exciting opportunity for complex analyses of problems throughout the healthcare domain. Whereas many early works focused on a particular aspect of patient care, often drawing on data from a specific clinical or administrative source, it has become clear such a single-source approach is insufficient to capture the complexity of the human condition. Instead, adequately modeling health and wellness problems requires the ability to draw upon data spanning multiple facets of an individual's biology, their care, and the social aspects of their life. Although such an awareness has greatly expanded the breadth of health and wellness data collected, the diverse array of data sources and intended uses often leave researchers and practitioners with a scattered and fragmented view of any particular patient. As a result, there exists a clear need to catalogue and organize the range of healthcare data available for analysis. This work represents an effort at developing such an organization, presenting a patient-centric framework deemed the Healthcare Data Spectrum (HDS). Comprised of six layers, the HDS begins with the innermost micro-level omics and macro-level demographic data that directly characterize a patient, and extends at its outermost to aggregate population-level data derived from attributes of care for each individual patient. For each level of the HDS, this manuscript will examine the specific types of constituent data, provide examples of how the data aid in a broad set of research problems, and identify the primary terminology and standards used to describe the data.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"2 3","pages":"248-271"},"PeriodicalIF":5.4,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Needs Assessment-mHealth Applications for People Aging with Multiple Sclerosis.","authors":"Ljilja Ruzic, Jon A Sanford","doi":"10.1007/s41666-018-0023-z","DOIUrl":"https://doi.org/10.1007/s41666-018-0023-z","url":null,"abstract":"<p><p>Multiple sclerosis (MS) is a complex inflammatory disorder of the central nervous system. It is characterized by a large number and variety of symptoms, with cognitive changes and mobility limitations being the most significant ones related to disability. A majority of individuals diagnosed with MS experience a major decline in their abilities due to the progression of MS after 5 years post-diagnosis. Following this period, they need to learn how to cope with the functional limitations caused by the disease and how to age with MS due to an early onset of age-related problems. As a result, they have to manage the effects of the condition on their lives every day. Self-management can help mitigate the symptoms associated with MS. Mobile health (mHealth) apps provide potential support for self-management of the condition as they represent robust technologies that have potential to include all the interventions proven to be useful to manage multiple health problems. However, none of the mobile applications on the market for people with MS present the holistic and integrative app that provides their users with a variety of the valuable functional features for the self-management of their health. Furthermore, there is a lack of literature on needs and concerns of individuals aging with MS to inform the design of the mobile technologies and related functional features of the MS-specific mobile apps. The purpose of this paper is to report the results of a qualitative study with individuals aging with MS, to (1) understand their health and wellness self-management needs, and (2) recognize the opportunities to meet those needs through mobile technologies and specific functional features. A systematic review of the functional features in MS-specific mobile applications is presented with the purpose to understand the current state of the utility of mobile apps and to identify two applications with the most versatile functionality.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"2 1-2","pages":"71-98"},"PeriodicalIF":4.6,"publicationDate":"2018-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148595267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases.","authors":"S M Shamimul Hasan, Edward A Fox, Keith Bisset, Madhav V Marathe","doi":"10.1007/s41666-017-0010-9","DOIUrl":"https://doi.org/10.1007/s41666-017-0010-9","url":null,"abstract":"<p><p>Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop <b>EpiK</b>-a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks-aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed-the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. We show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"1 2","pages":"260-303"},"PeriodicalIF":5.4,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Communication in the Electronic Age: an Analysis of Face-to-Face Physician-Nurse Communication in the Emergency Department.","authors":"Natalie C Benda, A Zachary Hettinger, Ann M Bisantz, Daniel J Hoffman, Nicolette M McGeorge, Akhila Iyer, Rebecca L Berg, Emilie M Roth, Ella S Franklin, Shawna J Perry, Robert L Wears, Rollin J Fairbanks","doi":"10.1007/s41666-017-0008-3","DOIUrl":"https://doi.org/10.1007/s41666-017-0008-3","url":null,"abstract":"<p><p>We describe the patterns and content of nurse to physician verbal conversations in three emergency departments (EDs) with electronic health records. Emergency medicine physicians and nurses were observed for 2 h periods. Researchers used paper notes to document the characteristics (e.g., partners involved, location of communication, who initiated communication) and content of nurse to physician conversations. Eighteen emergency nurses and physicians (nine each) were observed for a total of 36 h. Two hundred and fifty-five unique communication events were recorded across three emergency departments spread evenly across day, evening, and night shifts. A qualitative analysis of communication event content revealed 5 types of communication and 13 content themes. Content themes covered a broad range of topics including exchange of patient health information, management of the ED, and coordination of orders. Physician participants experienced significantly more communication events than nurse participants, while nurses initiated significantly more communication events than physicians. Most of the communication events occurred at the physician workstation followed by patient treatment areas. This study describes nature of verbal nurse to physician communication in the ED. Direct communication is still used to communicate important information, such as information about patients' status, in EDs with established electronic health records. Our results provide an overview of information exchanged in the ED which can serve as a basis for designing improved information support systems.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"1 2","pages":"218-230"},"PeriodicalIF":4.6,"publicationDate":"2017-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148595295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design Factors of Longitudinal Smartphone-based Health Surveys.","authors":"Sudip Vhaduri, Christian Poellabauer","doi":"10.1007/s41666-017-0003-8","DOIUrl":"https://doi.org/10.1007/s41666-017-0003-8","url":null,"abstract":"<p><p>Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness. Often, subjects respond to such surveys once, but it has become increasingly important to capture their responses multiple times over an extended period to accurately and quickly detect and track changes. With the help of smartphones, it is now possible to automate such longitudinal data collections, e.g., push notifications can be used to alert a subject whenever a new survey is available. This paper investigates various design factors of a longitudinal smartphone-based health survey data collection that contribute to user compliance and quality of collected data. This work presents the design recommendations based on analysis of data collected from 17 subjects over a 1-month period.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"1 1","pages":"52-91"},"PeriodicalIF":5.4,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}