bims-librar Biomed News
on Biomedical librarianship
Issue of 2019‒05‒19
twelve papers selected by
Thomas Krichel
Open Library Society


  1. Health Info Libr J. 2019 May 14.
    Kinengyere AA.
      This article is part of a series in this regular feature which looks at new directions in health science libraries. This paper highlights new initiatives aimed at ensuring health libraries can contribute to the development of Uganda in the 21st century and the challenges facing libraries. It stresses that for libraries to be successful they need to form networks and collaborations for resource sharing; take advantage of the benefits of information technology; computerise their library systems; as well as invest in the development of staff. The paper highlights the main challenge facing the library service as inadequate funding both from government for public-funded health libraries and the private sector (for privately funded health libraries). The paper concludes that, despite the bottlenecks brought about by inadequate funding, Ugandan health libraries have taken positive steps to support health research and education, as well as patient care, not just for Uganda, but for the whole of the East African region. J.M.
    Keywords:  Africa, East; ICT training; access to information; case reports; collaboration; information skills; librarianship, health science; national strategies
    DOI:  https://doi.org/10.1111/hir.12258
  2. Health Commun. 2019 May 15. 1-12
    Zhang L, Jung EH, Chen Z.
      Guided by the three-stage model and the integration of key constructs from the Health Belief Model, this study proposed a conceptual model to delve into the underlying mechanism linking health information seeking to psychological well-being. A nationwide online survey was administered, involving 522 WeChat users in China. The results of structural equation modeling analysis showed that health consciousness and WeChat health information reliability are significant antecedents predicting users' health information-seeking actions on WeChat, while perceived susceptibility, severity, and self-efficacy did not have significant relationships with such behavior. As predicted, the positive relationship between WeChat health information seeking and psychological well-being is mediated by perceived social support. The findings of this study provide both theoretical and practical implications to guide the use of mobile social media as a milieu for health improvement.
    DOI:  https://doi.org/10.1080/10410236.2019.1613479
  3. Med Care. 2019 Jun;57 Suppl 6 Suppl 2 S176-S183
    Khoong EC, Le GM, Hoskote M, Rivadeneira NA, Hiatt RA, Sarkar U.
      INTRODUCTION: In order to address health disparities, it is important to understand how vulnerable individuals seek information. This study used an adapted version of the Health Information National Trends Survey (HINTS) administered in English, Spanish, and Chinese to describe the behaviors and preferences of a diverse group of vulnerable urban residents.METHODS: We administered a modified HINTS survey in English, Spanish, and Chinese and used purposive sampling to ensure 50% were non-English speakers evenly divided between Spanish and Chinese speakers, and 50% of English-speakers identified as Black. We used multivariable logistic regression to determine characteristics associated with sources used for health information and preferences for delivery of health information.
    RESULTS: Among 1027survey respondents (514 English, 256 Spanish, 260 Chinese), 55% had adequate health literacy, and 50% reported household income <$20,000, but 77% reported owning a smartphone. A plurality sought health information on the Internet (39%) or from a health care provider (36%). In multivariable analyses, smartphone ownership predicted higher odds of seeking health information on the Internet [odds ratio, (OR) 2.98; 95% confidence interval (CI), 1.81-4.91]. Participants most preferred email (41%) and brochures (40%) for delivery of health information, but non-English survey respondents were less likely to prefer email: Spanish (OR, 0.30; 95% CI, 0.11-0.83) and Chinese (OR, 0.25; 95% CI, 0.09-0.71). Smartphone ownership predicted an email preference (OR, 2.19; 95% CI, 1.43-3.36).
    CONCLUSIONS: Among vulnerable populations, smartphone ownership and language preferences impact preferences for seeking and receiving health information. These preferences need to be considered in designing health messages.
    DOI:  https://doi.org/10.1097/MLR.0000000000001050
  4. J Telemed Telecare. 2019 May 12. 1357633X19846252
    Drosatos G, Kaldoudi E.
      INTRODUCTION: eHealth emerged as an interdisciplinary research area about 70 years ago. This study employs probabilistic techniques to semantically analyse scientific literature related to the field of eHealth in order to identify topics and trends and discuss their comparative evolution.METHODS: Authors collected titles and abstracts of published literature on eHealth as indexed in PubMed. Basic statistical and bibliometric techniques were applied to overall describe the collected corpus; Latent Dirichlet Allocation was employed for unsupervised topics identification; topics trends analysis was performed, and correlation graphs were plotted were relevant.
    RESULTS: A total of 30,425 records on eHealth were retrieved from PubMed (all records till 31 December 2017, search on 8 May 2018) and 23,988 of these were included to the study corpus. eHealth domain shows a growth higher than the growth of the entire PubMed corpus, with a mean increase of eHealth corpus proportion of about 7% per year for the last 20 years. Probabilistic topics modelling identified 100 meaningful topics, which were organised by the authors in nine different categories: general; service model; disease; medical specialty; behaviour and lifestyle; education; technology; evaluation; and regulatory issues.
    DISCUSSION: Trends analysis shows a continuous shift in focus. Early emphasis on medical image transmission and system integration has been replaced by increased focus on standards, wearables and sensor devices, now giving way to mobile applications, social media and data analytics. Attention on disease is also shifting, from initial popularity of surgery, trauma and acute heart disease, to the emergence of chronic disease support, and the recent attention to cancer, infectious disease, mental disorders, paediatrics and perinatal care; most interestingly the current swift increase is in research related to lifestyle and behaviour change. The steady growth of all topics related to assessment and various systematic evaluation techniques indicates a maturing research field that moves towards real world application.
    Keywords:  Latent Dirichlet Allocation; eHealth; topic modelling; trends analysis
    DOI:  https://doi.org/10.1177/1357633X19846252
  5. Health Info Libr J. 2019 May 14.
    Chen D, Zhang R, Feng J, Liu K.
      BACKGROUND: Online health communities (OHCs) experience difficulties in utilising patient reported posts to fulfil the information needs of online patients concerning health related issues.OBJECTIVES: We aim to propose a comprehensive method that leverages medical domain knowledge to extract useful information from posts to fulfil information needs of online patients.
    METHODS: A knowledge representation framework based on authoritative knowledge sources in the medical field for the OHC is proposed. On the basis of the framework, a health related information extraction process for analysing the posts in the OHC is proposed. Then, knowledge support rate (KSR) and effective information rate (EIR) are introduced as metrics to evaluate changes in knowledge extracted from the knowledge sources in terms of fulfilling the information needs of patients in the OHC.
    RESULTS: On the basis of a data set with 372 343 posts in an OHC, experimental results indicate that our method effectively extracts relevant knowledge for online patients. Moreover, KSR and EIR are feasible metrics of changes in knowledge in terms of fulfilling the information needs.
    CONCLUSIONS: The OHCs effectively fulfil the information needs of patients by utilising authoritative domain knowledge in the medical field. Knowledge based services for online patients facilitate an intelligent OHC in the future.
    Keywords:  China: consumer health information; health information needs; health literacy; information seeking behaviour
    DOI:  https://doi.org/10.1111/hir.12253
  6. Laryngorhinootologie. 2019 Mar;98(S 01): S290-S333
    Ihler F, Canis M.
      The wide distribution and availability of the internet during the last decades revolutionized the human information and communication behavior. Via internet, information can be easily retrieved and participative applications allow new types of interaction. The healthcare system is directly affected because information and communication represent a relevant part of it. The present contribution is intended to describe this development and its impact on otorhinolaryngology. The use of the internet for the research of health-related information is continuously increasing since several years and has meanwhile achieved significant importance. In the clinical context, other information sources still have a higher relevance. Laypeople mostly use the search engine of Google when performing health-related research. Even if the reliability of the presented information is difficult to assess, alternative offers that are specialized on valid healthcare information could not prevail. Anecdotic or incorrect information are regularly observed. Numerous trials investigated the quality of healthcare information on web pages. The methodical spectrum reaches from formula-depending readability testing via structured assessment tools up to certificates. The result shows that healthcare information on internet sites is often difficult to understand for the general population. Nearly all social media contain healthcare information and their relevance is increasing. Nonetheless, there is only few scientific knowledge on the characteristics and the effect of healthcare information in social media. The availability of online healthcare information requires new understanding of health literacy. The concept of digital literacy (eHealth literacy) contains among others the readability, media competence, IT knowledge, and basic scientific knowledge. The implementation of those skills depends on individual and social factors such as education, socio-economic status, and age. Investigations revealed a low healthcare literacy in a high percentage of the patients.The distribution of the internet also modifies the relationship between physician and patient. Well-informed patients request being involved in medical decisions. Physicians have a particular responsibility regarding the consultation of medical laypeople by weighting and verifying information. By actively participating, physicians should contribute to digitization in medicine for the benefit of their patients. Medical associations are particularly invited to contribute to this process.
    DOI:  https://doi.org/10.1055/a-0801-2585
  7. Psychiatr Q. 2019 May 16.
    Starcevic V, Baggio S, Berle D, Khazaal Y, Viswasam K.
      Cyberchondria denotes repeated online searches for health information that are associated with increasing levels of health anxiety. The aims of this study were to apply network analysis to investigate the extent to which cyberchondria is a distinct construct, ascertain which of the related constructs have the strongest relationships with cyberchondria and investigate whether some of the symptoms of cyberchondria are more central to the construct of cyberchondria. Questionnaires assessing the severity of cyberchondria, health anxiety, obsessive-compulsive disorder symptoms, intolerance of uncertainty, problematic Internet use, anxiety, depression and somatic symptoms were administered to 751 participants who searched for health information online during a previous 3-month period and were recruited from an online crowdsourcing platform. Network analyses were used to compute the networks, perform community detection tests and calculate centrality indices. Results suggest that cyberchondria is a relatively specific syndrome-like construct, distinct from all related constructs and consisting of interrelated symptoms. It has the strongest relationships with problematic Internet use and health anxiety. No symptom of cyberchondria emerged clearly as more central to the construct of cyberchondria. Future research should aim to deepen our understanding of cyberchondria and its links with psychopathology, especially its close relationship with problematic Internet use.
    Keywords:  Cyberchondria; Health anxiety; Network analysis; Online health information; Problematic internet use
    DOI:  https://doi.org/10.1007/s11126-019-09640-5
  8. J Med Internet Res. 2019 May 14. 21(5): e11931
    Din HN, McDaniels-Davidson C, Nodora J, Madanat H.
      BACKGROUND: Internet use for health information is important, given the rise of electronic health (eHealth) that integrates technology into health care. Despite the perceived widespread use of the internet, a persistent "digital divide" exists in which many individuals have ready access to the internet and others do not. To date, most published reports have compared characteristics of internet users seeking health information vs nonusers. However, there is little understanding of the differences between internet users seeking health information online and users who do not seek such information online. Understanding these differences could enable targeted outreach for health interventions and promotion of eHealth technologies.OBJECTIVE: This study aims to assess population-level characteristics associated with different types of internet use, particularly for seeking online health information.
    METHODS: The 2015-2016 California Health Interview Survey datasets were used for this study. Internet use was classified as never used the internet (Never use), ever used the internet but not to search for health information in the last 12 months (Use not for health), and ever used the internet and have used it to search for health information in the last 12 months (Use for health). Weighted multinomial logistic regression was used to assess sociodemographic and health characteristics associated with types of internet use. Findings are reported as odds ratios (ORs) with 95% CIs.
    RESULTS: Among 42,087 participants (weighted sample of 29,236,426), 19% reported Never Use of the internet, 27.9% reported Use not for health, and 53.1% reported Use for health. Compared to Never Use individuals, Use for health individuals were more likely to be younger (OR: 0.1, 95% CI 0.1-0.2 for ≥60 years vs <60 years), female (OR: 1.6, 95% CI 1.3-1.9 compared to males), and non-Hispanic white (OR: 0.54, 95% CI 0.4-0.7 for Latinos and OR: 0.2, 95% CI 0.2-0.4 for African Americans) and have a higher socioeconomic status (>400% of Federal Poverty Guidelines; OR: 1.3, 95% CI 1.4-2.4). Overall, characteristics for the Use not for health and Use for health groups were similar, except for those with lower levels of education and respondents not having visited a physician in the last year. For these two characteristics, the Use not for health group was more similar to the Never Use group.
    CONCLUSIONS: Our findings indicate that a digital divide characterized by sociodemographic and health information exists across three types of users. Our results are in line with those of previous studies on the divide, specifically with regard to disparities in use and access related to age, race/ethnicity, and socioeconomic status. Disparities in online health-seeking behavior may reflect existing disparities in health care access extending into a new era of health technology. These findings support the need for interventions to target internet access and health literacy among Never Use and Use not for health groups.
    Keywords:  digital divide; eHealth; health information; internet; online health information seeking; patient portals
    DOI:  https://doi.org/10.2196/11931
  9. Nurse Educ Today. 2019 Jul;pii: S0260-6917(19)30108-X. [Epub ahead of print]78 50-56
    Rathnayake S, Senevirathna A.
      BACKGROUND: As nursing students are the future workforce in nursing, they should have the necessary skills to find, understand and apply health information available on electronic platforms into their practice.OBJECTIVES: To assess eHealth literacy skills and associated factors among nursing students.
    DESIGN: Cross-sectional survey.
    SETTING: A Government School of Nursing, Sri Lanka.
    PARTICIPANTS: A purposive sample of 440 nursing students.
    METHOD: A self-administered questionnaire consisting of socio-demographic data, the questions related to the internet use and eHealth Literacy skills was used. In data analysis, descriptive statistics, the Mann-Whitney U test and Kruskal-Wallis H test were applied.
    RESULTS: The sample consisted of 440 nursing students (420 females and 20 males). The mean eHealth literacy score was 28.02 (SD ± 4.60). Nearly half of the respondents (49.4%) reported inadequate eHealth literacy skills. The respondents reported comparatively poor skills in differentiating high-quality health resources from low-quality health resources on the internet and the ability to use information from the internet to make health decisions. The majority viewed that including information technology (IT) as a subject into the nursing curriculum was very (50.7%) or absolutely (33.6%) important. The influencing factors of eHealth literacy skills of nursing students were self-rated internet skills (P = < 0.001), perception towards using the internet in health decision making (P = 0.009) and using the internet to access health resources (P = 0.001).
    CONCLUSION: Half of the nursing students have inadequate eHealth literacy skills, particularly skills in identifying trusted health resources and using this information in health decision making indicating the need for improving eHealth literacy skills among nursing students. A positive attitude towards the internet has a significant role in developing eHealth literacy skills. Improving competencies in eHealth literacy skills of nursing students is essential. Introducing these concepts into curricula, planning target interventions, and enhancing IT facilities within the educational environment are essential.
    Keywords:  Internet; Nursing students; Sri Lanka; eHealth literacy
    DOI:  https://doi.org/10.1016/j.nedt.2019.04.006
  10. JMIR Med Inform. 2019 May 10. 7(2): e12596
    Arbabi A, Adams DR, Fidler S, Brudno M.
      BACKGROUND: Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents.OBJECTIVE: We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology.
    METHODS: We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space. It uses the hierarchical structure provided by the biomedical ontology as an implicit prior embedding to better learn embedding of various terms. We trained our model on two biomedical ontologies-the Human Phenotype Ontology (HPO) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT).
    RESULTS: We tested our model trained on HPO by using two different data sets: 288 annotated PubMed abstracts and 39 clinical reports. We achieved 1.7%-3% higher F1-scores than those for our strongest manually engineered rule-based baselines (P=.003). We also tested our model trained on the SNOMED-CT by using 2000 Intensive Care Unit discharge summaries from MIMIC (Multiparameter Intelligent Monitoring in Intensive Care) and achieved 0.9%-1.3% higher F1-scores than those of our baseline. The results of our experiments show high accuracy of our model as well as the value of using the taxonomy structure of the ontology in concept recognition.
    CONCLUSION: Most popular medical concept recognizers rely on rule-based models, which cannot generalize well to unseen synonyms. In addition, most machine learning methods typically require large corpora of annotated text that cover all classes of concepts, which can be extremely difficult to obtain for biomedical ontologies. Without relying on large-scale labeled training data or requiring any custom training, our model can be efficiently generalized to new synonyms and performs as well or better than state-of-the-art methods custom built for specific ontologies.
    Keywords:  biomedical ontologies; concept recognition; human phenotype ontology; machine learning; medical text mining; phenotyping
    DOI:  https://doi.org/10.2196/12596
  11. IEEE/ACM Trans Comput Biol Bioinform. 2019 May 10.
    Liu J, Liu Q, Zhang L, Su S, Liu Y.
      Publishing biological data in XML formats is attractive for organizations who would like to provide their bioinformatics resources in an extensible and machine-readable format. In the era of big data, massive XML-based biological data management is emerged as a challengeable issue. With the continuous growth of the XML-based biological data sets, it is usually frustrating to use traditional declarative query languages to provide efficient query capabilities in terms of processing speed and scale. In this study, we report a novel platform to store and query massive XML-based biological data collections. A prototype tool for constructing HBase tables from XML-based biological data collections is firstly developed, and then a formal approach to transform the XML query model into the MapReduce query model is proposed. Finally, an evaluation of the query performance of the proposed approach on the existing XML-based biological databases is presented, showing that the performance advantages of the proposed solution. The source code of the massive XML-based biological data management platform is freely available at https://github.com/lyotvincent/X2H.
    DOI:  https://doi.org/10.1109/TCBB.2019.2915811