2nd Edition of Data Science for Health Services

Dear Colleagues,

We are pleased to announce a Special Issue on “Data Science for Health Services II”. In recent years, health services have been transformed by the emergence and increased application of data science methods such as predictive modeling, visualization, and artificial intelligence. These methods are being used for service planning, service management, and the delivery of care, thus improving the health of individuals and communities. Research on data science methods for health services can be broadly grouped into three stages:

• At the collection stage, data need to be acquired, stored safely and effectively, and occasionally combined. Data may include demographic and clinical information obtained from electronic medical records (EMRs), insurance claims, and other administrative data, as well as data continuously flowing from devices grouped under the Internet of Things (IoT). Recent innovations include virtual hospitals, wearable biosensors, digital health apps, and smart monitors. New data warehouse designs are often sought after to handle constraints such as privacy preservation, the large scale of records, and the need to efficiently support various queries. Finally, data fusion is required to augment common sources with value-added information or derive comprehensive measures for health service performance (e.g., quality index).
• At the analysis and forecasting stage, artificial intelligence (AI) allows for the exploration of patterns or the assessment of possible future scenarios. Machine learning (ML) techniques can serve to predict healthcare outcomes such as quality, utilization, or cost. Modeling and simulation (M&S) provides estimates for scenarios, such as the impact of a vaccination scheme on the number of beds in intensive care units. ML and M&S both face challenges in terms of data (e.g., insufficient data for emerging problems and conflicting measures) and algorithmic efficiency (e.g., scaling to big data).
• The adoption of data science methods in health services sheds light on how to translate results into actions that improve care for individuals and better meet the health needs of communities. Such translational efforts include novel multidisciplinary initiatives which bridge academic or organizational silos such as, for example, when social scientists, epidemiologists, and modelers create joint frameworks. The adoption of these methods also needs to navigate regulatory and legal frameworks, particularly in a changing ecosystem (e.g., new laws on data protection) and, given the emergence of new approaches, to safely perform computations (e.g., federated learning and secure enclaves).

We solicit papers for this Special Issue that broadly deal with such challenges by addressing open questions, providing novel case studies, or encouraging interesting and challenging debates. Papers can be reviews, syntheses, viewpoints, meta-analyses, or original research articles.

Dr. Philippe J. Giabbanelli
Dr. Francisco Iacobelli
Dr. Charlotte James
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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.

• clinical decision support
• clinical care models
• health informatics
• quality of care
• population health planning
• digital health