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Advances in Health Technology Assessment in the Era of AI...
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Advances in Health Technology Assessment in the Era of AI and Data Science

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2876

Editors

Dr. Z. Kevin Lu

E-Mail Website
Guest Editor
College of Pharmacy, University of South Carolina, Columbia, SC 29208, USA
Interests: health economics; health policy; health technology assessment; real-world study
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Yuan

E-Mail Website
Guest Editor
Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai 201203, China
Interests: health economics; health policy; health technology assessment; real-world study
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to invite your submissions for a Special Issue of the journal Healthcare, titled "Advances in Health Technology Assessment in the Era of AI and Data Science". This issue seeks to highlight the most recent advancements and innovations in health technology assessment (HTA), with a particular emphasis on clinical effectiveness research, pharmacoeconomics, and evaluation of the economic burden and safety of new technologies.

In our rapidly advancing technological landscape, HTA is crucial for ensuring that new health technologies are both safe and effective, as well as economically viable. This Special Issue aims to gather and showcase the latest research and insights from experts in the field, providing valuable contributions that can guide healthcare decision making.

We especially encourage submissions that explore the role of artificial intelligence (AI) in health technology assessment. AI offers unique opportunities to enhance the accuracy and efficiency of HTA processes, and we are eager to see how these technologies are being integrated into healthcare evaluations. Given the rapid development in AI and big data, contributions from international researchers are particularly welcome.

There are some aspects that might not fully align with our scope, such as the pharmaceutical industry, drug price studies, business, or procurement. However, the emphasis of benefits to patient outcomes or healthcare delivery can be considered.

We invite researchers, educators, and policymakers to contribute their findings and perspectives. Your work will help foster a comprehensive understanding of the advancements shaping the future of health technology assessment.

Dr. Z. Kevin Lu
Dr. Jing Yuan
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 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-anonymized 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 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 2700 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 technology assessment (HTA)
  • artificial intelligence
  • big data

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 1204 KB  
Article
The Complex Relationship Between HDL/LDL Cholesterol, Stroke and Cardiovascular Disease
by Mark Parker, Tanja Novaković, Milica Krga Rastović, Vanesa Benković and Iñaki Gutierrez-Ibarluzea
Healthcare 2026, 14(10), 1371; https://doi.org/10.3390/healthcare14101371 - 17 May 2026
Viewed by 574
Abstract
Background and Aims: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of mortality worldwide, with lipid abnormalities playing a central role in disease development. While the causal role of low-density lipoprotein cholesterol (LDL-C) in ASCVD is well-established, the long-term population impact of [...] Read more.
Background and Aims: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of mortality worldwide, with lipid abnormalities playing a central role in disease development. While the causal role of low-density lipoprotein cholesterol (LDL-C) in ASCVD is well-established, the long-term population impact of combined lipid profiles, particularly the HDL-C/LDL-C ratio, remains less clearly quantified. This study aimed to estimate the lifetime burden of cardiovascular outcomes associated with different lipid risk profiles using a patient-level simulation framework. Methods: The authors analyzed projected lifetime ASCVD events across four HDL-C/LDL-C risk strata, ranging from low (≥0.45) to very high (<0.25), using the National Health Model Database of Projected and Estimated Outcomes (NHM-DPEO)—a digital twin of national healthcare systems built from multiple data sources, including national health and demographic statistics and estimates from the relevant literature. The framework is structured as a patient-level simulation model that projects individual health trajectories over a lifetime horizon. Model outputs were assessed for plausibility by comparison with published epidemiological estimates. Results: The NHM simulation revealed a strong, graded relationship between lipid profiles and cardiovascular survival. Life expectancy declined from 80.2 years in the lowest risk group (HDL-C/LDL-C ≥ 0.45) to 63.0 years in the very-high-risk group (HDL-C/LDL-C < 0.25), a reduction of 17.2 years, with 13.7 fewer QALYs. Similarly, participants with LDL-C > 5.0 mmol/L had a life expectancy 13.4 years shorter than those with LDL-C < 3.1 mmol/L. The burden of ASCVD increased exponentially with worsening lipid ratios: MI events rose from 5000 to 73,090 per 100,000 births, with onset in the highest risk group occurring as early as age 20. Ischaemic heart disease followed a similar pattern, showing up to 92% of events attributable to elevated lipid risk. While ischaemic stroke risk displayed a more complex pattern due to earlier MI mortality in high-risk groups, overall cardiovascular mortality and lifetime event burden were dominated by LDL-driven disease. These findings demonstrate that sustained LDL-C reduction and balanced HDL-C/LDL-C ratios confer substantial survival benefits across both sexes and all age groups. Conclusions: This study shows that lipid balance has a decisive influence on cardiovascular survival. Sustained LDL-C reduction and favourable HDL-C/LDL-C ratios markedly extend life expectancy and delay the onset of MI and IHD. The magnitude of this survival benefit highlights the need for early and continuous lipid control as a cornerstone of ASCVD prevention. The NHM quantifies these lifetime effects, offering valuable insights for targeted strategies that improve both longevity and quality of life. Full article
(This article belongs to the Special Issue Advances in Health Technology Assessment in the Era of AI and Data Science)
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15 pages, 644 KB  
Article
Ethical, Legal, and Social Assessment of AI-Based Technologies for Prevention and Diagnosis of Rare Diseases in Health Technology Assessment Processes
by Pietro Refolo, Costanza Raimondi, Violeta Astratinei, Livio Battaglia, Josep M. Borràs, Paula Closa, Alessandra Lo Scalzo, Marco Marchetti, Sonia Muñoz-López, Laura Sampietro-Colom and Dario Sacchini
Healthcare 2025, 13(7), 829; https://doi.org/10.3390/healthcare13070829 - 4 Apr 2025
Cited by 3 | Viewed by 1654
Abstract
Background: While the HTA community appears well-equipped to assess preventive and diagnostic technologies, certain limitations persist in evaluating technologies designed for rare diseases, including those based on Artificial Intelligence (AI). In Europe, the EUnetHTA Core Model® serves as a reference for assessing [...] Read more.
Background: While the HTA community appears well-equipped to assess preventive and diagnostic technologies, certain limitations persist in evaluating technologies designed for rare diseases, including those based on Artificial Intelligence (AI). In Europe, the EUnetHTA Core Model® serves as a reference for assessing preventive and diagnostic technologies. This study aims to identify key ethical, legal, and social issues related to AI-based technologies for the prevention and diagnosis of rare diseases, proposing enhancements to the Core Model. Methods: An exploratory sequential mixed methods approach was used, integrating a PICO-guided literature review and a focus group. The review analyzed six peer-reviewed articles and compared the findings with a prior study on childhood melanoma published in this journal (Healthcare), retaining only newly identified issues. A focus group composed of experts in ethical, legal, and social domains provided qualitative insights. Results: Thirteen additional issues and their corresponding questions were identified. Ethical concerns related to rare diseases included insufficient disease history knowledge, lack of robust clinical data, absence of validated efficacy tools, overdiagnosis/underdiagnosis risks, and unknown ICER thresholds. Defensive medicine was identified as a legal issue. For AI-based technologies, concerns included discriminatory outcomes, explicability, and environmental impact (ethical); accountability and reimbursement (legal); and patient involvement and job losses (social). Conclusions: Integrating these findings into the Core Model enables a comprehensive HTA of AI-based rare disease technologies. Beyond the Core Model, these issues may inform broader assessment frameworks, ensuring rigorous and ethically responsible evaluations. Full article
(This article belongs to the Special Issue Advances in Health Technology Assessment in the Era of AI and Data Science)
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