Clinic of Social and Family Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece.
Background: Individuals with schizophrenia spectrum disorders use the Internet for general and health-related purposes. Their ability to find, understand, and apply the health information they acquire online in order to make appropriate health decisions - known as eHealth literacy - has never been investigated. The European agenda strives to limit health inequalities and enhance mental health literacy. Read More
Background: Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. Read More
Departments of Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO, USA.
Background: Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks. Read More
Evidence Synthesis & Modelling for Health Improvement (ESMI), Institute for Health Research, University of Exeter Medical School, University of Exeter, Room 3.09.3, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK.
Background: Expert opinion is often sought to complement available information needed to inform model-based economic evaluations in health technology assessments. In this context, we define expert elicitation as the process of encoding expert opinion on a quantity of interest, together with associated uncertainty, as a probability distribution. When availability for face-to-face expert elicitation with a facilitator is limited, elicitation can be conducted remotely, overcoming challenges of finding an appropriate time to meet the expert and allowing access to experts situated too far away for practical face-to-face sessions. Read More
Department of computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway.
Background: To improve consistency and streamline development and publication of clinical guidelines (GL), there is a need for appropriate software support. We have found few specific tools for the actual authoring and maintaining of GLs, and correspondingly few analyses or reviews of GL development tool functionality. In order to assist GL developers in selecting and evaluating tools, this study tries to address the perceived gap by pursuing four goals: 1) identifying available tools, 2) reviewing a representative group of tools and their supported functionalities, 3) uncovering themes of features that the studied tools support, and 4) compare the selected tools with respect to the themes. Read More
BMC Med Inform Decis Mak 2017 Aug 31;17(1):130. Epub 2017 Aug 31.
Department for Health Evidence, Radboudumc, Nijmegen, the Netherlands.
Background: There is increasing recognition of the delicate balance between the modest benefits of palliative chemotherapy and the burden of treatment. Decision aids (DAs) can potentially help patients with advanced cancer with these difficult treatment decisions, but providing detailed information could have an adverse impact on patients' well-being. The objective of this randomised phase II study was to evaluate the safety and efficacy of DAs for patients with advanced cancer considering second-line chemotherapy. Read More
Background: Hand hygiene is one of the most effective attempts to control nosocomial infections, and it is an important measure to avoid the transmission of pathogens. However, the compliance of healthcare workers (HCWs) with hand washing is still poor worldwide. Herein, we aimed to determine the best hand hygiene preference of the infectious diseases and clinical microbiology (IDCM) specialists to prevent transmission of microorganisms from one patient to another. Read More
Background: Despite the emergence of genomics-based risk prediction tools in oncology, there is not yet an established framework for communication of test results to cancer patients to support shared decision-making. We report findings from a stakeholder engagement program that aimed to develop a framework for using Markov models with individualized model inputs, including genomics-based estimates of cancer recurrence probability, to generate personalized decision aids for prostate cancer patients faced with radiation therapy treatment decisions after prostatectomy.
Methods: We engaged a total of 22 stakeholders, including: prostate cancer patients, urological surgeons, radiation oncologists, genomic testing industry representatives, and biomedical informatics faculty. Read More
Background: Unsatisfactory colposcopy, where the cells of interest are not visible in women with a positive cervical screening test, is a common area of clinical uncertainty due to the lack of clear evidence and guidance. Colposcopists' opinions and experiences are likely to have a significant influence on service provision and the development of national policy. The aim of this study was to analyse decision-making when applied to women with unsatisfactory colposcopy. Read More
Background: Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. Read More
Background: Identifying patients with certain clinical criteria based on manual chart review of doctors' notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR). This task can be automated using text classifiers based on Natural Language Processing (NLP) techniques along with pattern recognition machine learning (ML) algorithms. The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus (SLE) in comparison with a newer Bayesian word vector method. Read More
Background: Implementing patient decision aids in clinic workflow has proven to be a challenge for healthcare organizations and physicians. Our aim was to determine the organizational strategies, motivations, and facilitating factors to the routine implementation of Option Grid™ encounter decision aids at two independent settings.
Method: Case studies conducted by semi-structured interview, using the Normalization Process Theory (NPT) as a framework for thematic analysis. Read More
Background: The objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record XML extracts, both in isolation and concurrently. NoSQL database systems have recently attracted much attention, but few studies in the literature address their direct comparison with relational databases when applied to build the persistence layer of a standardized medical information system.
Methods: One relational and two NoSQL databases (one document-based and one native XML database) of three different sizes have been created in order to evaluate and compare the response times (algorithmic complexity) of six different complexity growing queries, which have been performed on them. Read More
Background: Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. Read More
Background: Standards and technical specifications have been developed to define how the information contained in Electronic Health Records (EHRs) should be structured, semantically described, and communicated. Current trends rely on differentiating the representation of data instances from the definition of clinical information models. The dual model approach, which combines a reference model (RM) and a clinical information model (CIM), sets in practice this software design pattern. Read More
BMC Med Inform Decis Mak 2017 Aug 14;17(1):121. Epub 2017 Aug 14.
Present address: National Centre for Epidemiology & Population Health, Australian National University, Canberra, ACT 2601, Australia.
Background: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases.
Methods: The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Read More
Background: Shared decision making is essential to patient centered care, but can be difficult for busy clinicians to implement into practice. Tools have been developed to aid in shared decision making and embedded in electronic medical records (EMRs) to facilitate use. This study was undertaken to explore the patterns of use and barriers and facilitators to use of two decision aids, the Statin Choice Decision Aid (SCDA) and the Diabetes Medication Choice Decision Aid (DMCDA), in primary care practices where the decision aids are embedded in the EMR. Read More
BMC Med Inform Decis Mak 2017 Aug 10;17(1):119. Epub 2017 Aug 10.
Regenstrief Institute, Inc., Center for Biomedical Informatics, 1101 W. 10th St, Indianapolis, IN, 46202, USA.
Background: Human Papillomavirus (HPV) leads to serious health issues and remains the most common sexually transmitted infection. Despite availability of effective vaccines, HPV vaccination rates are suboptimal. Furthermore, providers recommend the HPV vaccine less than half the time for eligible patients. Read More
Background: Cardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD. Read More
BMC Med Inform Decis Mak 2017 Aug 7;17(1):116. Epub 2017 Aug 7.
Department of Epidemiology and Biostatistics, University of Gondar, Gondar, Ethiopia.
Background: Using reliable information from routine health information systems over time is an important aid to improving health outcomes, tackling disparities, enhancing efficiency, and encouraging innovation. In Ethiopia, routine health information utilization for enhancing performance is poor among health workers, especially at the peripheral levels of health facilities. Therefore, this study aimed to assess routine health information system utilization and associated factors among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. Read More
Background: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Read More
Background: The internet is an increasingly relevant source of health information. We aimed to assess the quality of German dentists' websites on periodontitis, hypothesizing that it was significantly associated with a number of practice-specific parameters.
Methods: We searched four electronic search engines and included pages which were freely accessible, posted by a dental practice in Germany, and mentioned periodontal disease/therapy. Read More
BMC Med Inform Decis Mak 2017 Aug 1;17(1):113. Epub 2017 Aug 1.
Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT, 84108, USA.
Background: Genetic testing, especially in pharmacogenomics, can have a major impact on patient care. However, most physicians do not feel that they have sufficient knowledge to apply pharmacogenomics to patient care. Online information resources can help address this gap. Read More
BMC Med Inform Decis Mak 2017 Aug 1;17(1):112. Epub 2017 Aug 1.
Zuyd University of Applied Sciences, Nieuw Eyckholt 300, 6419 DJ, Heerlen, The Netherlands.
Background: A patient decision aid (PtDA) can support shared decision making (SDM) in preference-sensitive care, with more than one clinically applicable treatment option. The development of a PtDA is a complex process, involving several steps, such as designing, developing and testing the draft with all the stakeholders, known as alpha testing. This is followed by testing in 'real life' situations, known as beta testing, and then finalising the definite version. Read More
Background: The US Veterans Administration (VA) has developed a robust and mature computational infrastructure in support of its electronic health record (EHR). Web technology offers a powerful set of tools for structuring clinical decision support (CDS) around clinical care. This paper describes informatics challenges and design issues that were confronted in the process of building three Web-based CDS systems in the context of the VA EHR. Read More
Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. Read More
Background: A Personal Health Record (PHR) is an online application that allows patients to access, manage, and share their health data. PHRs not only enhance shared decision making with healthcare providers, but also enable remote monitoring and at-home-collection of detailed data. The benefits of PHRs can be maximized in insulin dose adjustment for patients starting or intensifying insulin regimens, as frequent self-monitoring of glucose, self-adjustment of insulin dose, and precise at-home data collection during the visit-to-visit period are important for glycemic control. Read More
Background: Due to the increasing availability of individual-level information across different electronic datasets, record linkage has become an efficient and important research tool. High quality linkage is essential for producing robust results. The objective of this study was to describe the process of preparing and linking national Brazilian datasets, and to compare the accuracy of different linkage methods for assessing the risk of stillbirth due to dengue in pregnancy. Read More
Background: Although data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice. Read More
Background: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion.
Methods: In this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. Read More
Background: To deliver evidence-based medicine, clinicians often reference resources that are useful to their respective medical practices. Owing to their busy schedules, however, clinicians typically find it challenging to locate these relevant resources out of the rapidly growing number of journals and articles currently being published. The literature-recommender system may provide a possible solution to this issue if the individual needs of clinicians can be identified and applied. Read More
School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, 77030, USA.
Background: The most important knowledge in the field of patient safety is regarding the prevention and reduction of patient safety events (PSE) during treatment and care. The similarities and patterns among the events may otherwise go unnoticed if they are not properly reported and analyzed. There is an urgent need for developing a PSE reporting system that can dynamically measure the similarities of the events and thus promote event analysis and learning effect. Read More
Background: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. Read More
Background: Automated methods for identifying clinically relevant new versus redundant information in electronic health record (EHR) clinical notes is useful for clinicians and researchers involved in patient care and clinical research, respectively. We evaluated methods to automatically identify clinically relevant new information in clinical notes, and compared the quantity of redundant information across specialties and clinical settings.
Methods: Statistical language models augmented with semantic similarity measures were evaluated as a means to detect and quantify clinically relevant new and redundant information over longitudinal clinical notes for a given patient. Read More
In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) held on December 8-10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. At ICIBM 2016, a special theme, "Medical Informatics and Big Data," was dedicated to the recent advances of data science in the medical domain. After peer review, ten articles were selected for this special issue, covering topics such as Knowledge and Data Personalization, Social Media Applications to Healthcare, Clinical Natural Language Processing, Patient Safety Analyses, and Data Mining Using Electronic Health Records. Read More
Background: On average, 570 million users, 93% in China's first-tier cities, log on to WeChat every day. WeChat has become the most widely and frequently used social media in China, and has been profoundly integrated into the daily life of many Chinese people. A variety of health-related information may be found on WeChat. Read More
Background: Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus towards the development of ontologies for the public health domain and use patient-centric sources from MedlinePlus related to HPV-causing cancers. Read More
Background: Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.
Methods: In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Read More
Background: We develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Read More
Background: To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Read More
Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
Background: NHS England has mandated the use in hospital laboratories of an automated early warning algorithm to create a consistent method for the detection of acute kidney injury (AKI). It generates an 'alert' based on changes in serum creatinine level to notify attending clinicians of a possible incident case of the condition, and to provide an assessment of its severity. We aimed to explore the feasibility of secondary data analysis to reproduce the algorithm outside of the hospital laboratory, and to describe the epidemiology of AKI across primary and secondary care within a region. Read More
Background: People with bipolar disorder often experience ill health and have considerably reduced life expectancies. Suboptimal treatment is common and includes a lack of effective medicines, overtreatment, and non-adherence to medical interventions and lifestyle measures. E- and m-health applications support patients in optimizing their treatment but often exhibit conceptual and technical shortcomings. Read More
Background: Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Read More
National Centre of Research Excellence in Nursing, Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, QLD, 4222, Australia.
Background: Advancements in technology are enabling patients to participate in their health care through self-monitoring and self-management of diet, exercise and chronic disease. Technologies allowing patients to participate in hospital care are still emerging but show promise. Our team is developing a program by which hospitalised patients can participate in their nutrition care. Read More
Background: Mobile phone-based technology has been used in improving the delivery of healthcare services in many countries. However, data on the effects of this technology on improving primary healthcare services in resource-poor settings are limited. The aim of this study is to develop and test a mobile phone-based system to improve health, population and nutrition services in rural Bangladesh and evaluate its impact on service delivery. Read More
Background: With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. Read More
Background: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI).
Methods: This prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006-2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Read More
Background: mHealth holds the potential to educate rural communities in developing countries such as Malawi, on issues which over-burdened and under staffed health centres do not have the facilities to address. Previous research provides support that mHealth could be used as a vehicle for health education campaigns at a community level; however the limited involvement of potential service users in the research process endangers both user engagement and intervention effectiveness.
Methods: This two stage qualitative study used participatory action research to inform the design and development of an mHealth education intervention. Read More