Special Issue "Health and Medical Policy in the Era of Big Data Analytics"
Deadline for manuscript submissions: 31 January 2023 | Viewed by 1464
Interests: health/medical policies; organ transplantation and other chronic disease treatments; biostatistics.
Interests: end-of-life issues: advance care planning and grief process; patients’ autonomy and dignity in health care settings; personalized music intervention for dementia
Over the last few years, the breathtaking pace of advances in translational medical knowledge has been generating vast quantities of digitized biomedical and health care data that more than match the quintessential five Vs of Big Data—Volume, Velocity, Variety, Variability, and Veracity.
It is only natural that proven and new techniques and solutions based on Big Data analytics are developed and employed in processing zetta- and yotta-bytes of digitized biomedical/health data. Some of the key objectives of using Big Data analytics for the biomedical and health sectors are:
- To generate sensible knowledge that provides fresh insights;
- To help craft actionable personalized diagnostics, medical care, and precision treatment;
- To potentially create new revenue streams for the future growth of the health care industry while saving operational costs.
As the fusion of biomedical research with Big Data continues to break new grounds, a fresh examination of the adequacy of existing health and medical policies is warranted. Such an examination would involve in-depth analyses with Big Data to identify loopholes, address burdensome statutes, and discover what may be missing and what is required.
These essential steps would greatly help in formulating sound health and medical policies that would not only promote continued and balanced growth of the sector, but also enhance critical aspects such as basic adherence to the Hippocratic Oath, the assurance of privacy, and accessible and equitable biomedical and health care.
To that end, this Special Issue on “Health and Medical Policy in the Era of Big Data Analytics” seeks original research, reports, and reviews that highlight challenges posed in developing actionable policies with Big Data analytics to provide state-of-the-art, affordable, and equitable biomedical health care to all. Research contributions showing how augmenting AI/machine learning techniques with Big Data analytics could help mitigate health care disparity are also welcome.
Prof. Dr. Naoru Koizumi
Prof. Dr. Megumi Inoue
Prof. Dr. Ali Andalibi
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- artificial intelligence/machine learning
- medical and health care policy
- fairness and disparity in the health care system