Special Issue "Health and Medical Policy in the Era of Big Data Analytics"

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Policy".

Deadline for manuscript submissions: 31 January 2023 | Viewed by 1464

Special Issue Editors

Prof. Dr. Naoru Koizumi
E-Mail Website
Guest Editor
Schar School of Policy and Government, George Mason University, Fairfax, VA 22030, USA
Interests: health/medical policies; organ transplantation and other chronic disease treatments; biostatistics.
Prof. Dr. Megumi Inoue
E-Mail Website
Guest Editor
Department of Social Work, College of Heatlth and Human Services, George Mason University, Fairfax, VA 22030, USA
Interests: end-of-life issues: advance care planning and grief process; patients’ autonomy and dignity in health care settings; personalized music intervention for dementia
Prof. Dr. Ali Andalibi
E-Mail Website
Guest Editor
School of Systems Biology, College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: microbiology and molecular genetics; inflammatory process; innate immune molecules in the etiology of diseases

Special Issue Information

Dear Colleagues,

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
Guest Editors

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.

Keywords

  • artificial intelligence/machine learning
  • medical and health care policy
  • fairness and disparity in the health care system

Published Papers (2 papers)

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Research

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Article
Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models
Healthcare 2022, 10(5), 892; https://doi.org/10.3390/healthcare10050892 - 12 May 2022
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Abstract
No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction [...] Read more.
No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893–0.895) in all patients’ data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors’ loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes. Full article
(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)
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Review

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Review
A Bibliometric and Visualization Analysis of Motor Learning in Preschoolers and Children over the Last 15 Years
Healthcare 2022, 10(8), 1415; https://doi.org/10.3390/healthcare10081415 - 28 Jul 2022
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Abstract
Motor learning enables preschoolers and children to acquire fundamental skills that are critical to their development. The current study sought to conduct a bibliometric and visualization analysis to provide a comprehensive overview of motor-learning progress in preschoolers and children over the previous 15 [...] Read more.
Motor learning enables preschoolers and children to acquire fundamental skills that are critical to their development. The current study sought to conduct a bibliometric and visualization analysis to provide a comprehensive overview of motor-learning progress in preschoolers and children over the previous 15 years. The number of studies is constantly growing, with the United States and Australia, as well as other productive institutions and authors, at the leading edge. The dominant disciplines were Neurosciences and Neurology, Psychology, Rehabilitation, and Sport Sciences. The journals Developmental Medicine & Child Neurology, Human Movement Science, Physical Therapy, Neuropsychology, Journal of Motor Behavior, and Journal of Experimental Child Psychology have been the most productive and influential in this regard. The most common co-citations for clinical symptoms were for cerebral palsy, developmental coordination disorder, and autism. Research has focused on language impairment (speech disorders, explicit learning, and instructor-control feedback), as well as effective intervention strategies. Advances in brain mechanisms and diagnostic indicators, as well as new intervention and rehabilitation technologies (virtual reality, transcranial magnetic stimulation, and transcranial direct current stimulation), have shifted research frontiers and progress. The cognitive process is critical in intervention, rehabilitation, and new technology implementation and should not be overlooked. Overall, our broad overview identifies three major areas: brain mechanism research, clinical practice (intervention and rehabilitation), and new technology application. Full article
(This article belongs to the Special Issue Health and Medical Policy in the Era of Big Data Analytics)
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