Data for Better Health

Call for papers for: Library Hi Tech

The wide adoption of electronic health records, increasing use of available digital health devices and applications, and the recent upsurge of wearable equipment all contribute to the vast amount of health data generated daily. Overall our health is being transformed from experience-based to data driven. Availability of huge volumes of data are setting the ground for evidence-based practice, precision medicine, and predictive analytics. In addition, health data has gone beyond health care facilities and been extended to the home, workplace, and communities. For better health outcomes, there is a call to focus on the connections between data, technology, and people, which demands an interdisciplinary effort.
 
Therefore, we are originating the first special issue - Data for Better Health - in the field of Library and Information Science, concentrating on the latest studies on health information technologies and health data. Although health data issues have been investigated by researchers and practitioners in biomedical and health sciences, this special issue calls for interdisciplinary participation to promote the connections between data, technology, and people for better health outcomes. Thus, theories, methods, models, or concepts from different disciplines are encouraged to address the big data challenge in health.

We encourage the submission of original works related to health data under the attention of researchers and practitioners in a variety of fields which include but are not limited to Library and Information Science, Computer Science, Medicine, Nursing, Pharmacy, and Public Health. This special issue will cover both technological and non-technological issues from the evidence, significant findings, experience, and best practices related to these rapidly growing and evolving areas. 

Topics

  • Big Data Challenges and Applications in Healthcare
  • Data for Better Health Outcomes
  • Electronic Health Records (EHRs)
  • Health Data Analytics and Visualization
  • Health Data Collection, Management, Processing, Sharing, or Curation
  • Health Data Ethics
  • Health Data Services and Practices
  • Health Information Behaviors
  • Health Information Systems and Applications
  • Health Data and Natural Language Processing / Machine Learning
  • Norms and Standards for Health Data
  • Patient Generated Health Data
  • Predictive Analytics in Healthcare
  • Health Surveys and Public Health Surveillance 
  • Wearable Technology and Electronic Devices


Important Dates

Structured abstracts due: May 1, 2019

Acceptance notification of abstracts: June 1, 2019

Full papers due: August 31, 2019

Acceptance notification of full papers: October 31, 2019

Final papers: December 31, 2019

Submission

Library Hi Tech is a peer-reviewed research journal. The journal is abstracted or indexed in Social Sciences Citation Index (SSCI), Scopus, and other major sources. Further information regarding the journal can be found on the journal web site (https://www.emeraldinsight.com/loi/lht).
 
Structured abstracts should be no more than 250 words in total (including keywords and article classification) and prepared by following author guidelines (http://emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=lht). Please send your abstract to: [email protected] or [email protected] by May 1, 2019.

Full papers (for accepted abstracts) should be 4000-8000 words (including references and appendices) and prepared using Library Hi Tech’s author guidelines (http://emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=lht). Please submit your paper directly to the online submission system (http://mc.manuscriptcentral.com/lht) and choose the Special Issue (Data for Better Health) in the system by August 31, 2019.

Both abstract and full paper submissions to the special issue will be screened by the Special Issue Editors to ensure that they conform to the quality standards of Library Hi Tech Journal. Abstracts that do not pass initial screening will be immediately returned to the authors. An accepted abstract does not guarantee the acceptance of a full paper.

Guest Editors

Dan Wu, PhD, Professor
School of Information Management
Wuhan University, China
E-mail: [email protected]

Fei Yu, PhD, MPS, Assistant Professor
School of Information and Library Science / Health Sciences Library
University of North Carolina at Chapel Hill, United States of America
E-mail: [email protected]