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Health Informatics: Human Health and Health Care Services

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 282

Special Issue Editor


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Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
Interests: explainable artificial intelligence; machine learning; artificial intelligence; medical informatics; bibliometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health informatics is a multidisciplinary field that uses technology and information systems to improve healthcare services. It is essentially the intersection of health, information technology,  computer science and software engineering. By utilizing digital health data, AI-supported data analytics, IoMT and agile approaches it can streamline health processes, increase patient safety and satisfaction, enhance patient-centered care and reduce resource consumption.

The association is symbiotic: health services provide the data and context, while health informatics provides the means to manage and interpret them. This relationship leads to significant benefits, such as improved clinical decision-making, better communication between providers and greater patient engagement through social networking. Ultimately, health informatics supports digital health transformation, leading to more efficient, safer and higher-quality care services at both the individual and population levels.

Prof. Dr. Peter Kokol
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • health informatics
  • health services
  • digital health transformation
  • artificial intelligence
  • machine learning
  • patient-centered care
  • IoMT

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Published Papers (1 paper)

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Review

21 pages, 2658 KB  
Review
Symbiosis in Health: The Powerful Alliance of AI and Propensity Score Matching in Real World Medical Data Analysis
by Peter Kokol, Bojan Žlahtič, Helena Blažun Vošner, Jernej Završnik and Tadej Završnik
Appl. Sci. 2026, 16(3), 1524; https://doi.org/10.3390/app16031524 - 3 Feb 2026
Abstract
The rapid expansion of real-world medical data is driving a transformative shift toward integrating artificial intelligence (AI) with propensity score matching (PSM) to enhance clinical research. While AI provides advanced capabilities in diagnostics and prediction, PSM serves as a critical statistical tool for [...] Read more.
The rapid expansion of real-world medical data is driving a transformative shift toward integrating artificial intelligence (AI) with propensity score matching (PSM) to enhance clinical research. While AI provides advanced capabilities in diagnostics and prediction, PSM serves as a critical statistical tool for mitigating confounding bias in quasi-experimental studies, thereby approximating the reliability of randomized controlled trials. This study utilized synthetic thematic analysis (STA) and bibliometric mapping via VOSviewer and Bibliometrix to analyze 433 documents retrieved from the Scopus database. The findings reveal an exponential growth in this field between 2020 and 2024, with the United States and China emerging as the primary contributors to global research output. Four central thematic clusters were identified: prediction, cancer management, diagnostics, and deep learning. The integration is bidirectional, characterized by AI algorithms optimizing propensity score estimation and PSM frameworks being used to enhance AI-driven models. This methodological convergence is significantly improving the rigour of observational studies, particularly in complex clinical domains such as cardiovascular disease and chronic illness management. Ultimately, the AI-PSM symbiosis represents a critical trend in medical informatics, refining the accuracy of predictive modelling and strengthening the evidentiary value of real-world data in global health research. Full article
(This article belongs to the Special Issue Health Informatics: Human Health and Health Care Services)
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