Simone Grassi
Francesco Ausania
Davide Ferorelli
Francesco De Micco- 1Department of Health Science, Section of Forensic Medical Sciences, University of Florence, Florence, Italy
- 2Department of Diagnostics and Public Health, Section of Forensic Medicine, University of Verona, Verona, Italy
- 3Section of Legal Medicine, Department of Interdisciplinary Medicine, University of Bari, Bari, Italy
- 4Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
Editorial on the Research Topic
Errors and biases in modern healthcare: public health, medico-legal and risk management aspects
Is to err (only) human nowadays? Error is traditionally considered an unavoidable part of human life and activities, and the phrase “to err is human” has been a sort of dogma in medicine for the last decades (1). The actual genesis of human error is still largely unexplored, and the interplay between hospital organization, enforcement of policies, and human choices is still far from being decrypted (2, 3). However, we do know that teamwork is the bedrock of modern medicine and relies on human paradigms like trust (4). Can we trust artificial intelligence (AI)? The question is misleading. AI is a technology able to find correlations between input variables, but it does not currently have the capacity to infer causal relationships (at least following the traditional rules of logic). AI has no professionality, no free will, no reputation: physicians must learn how to work with a technology suppressing the human need to trust it and how to supervise its outputs often without the possibility to understand its internal processes (4). A new kind of error—the bias (systematic error)—will emerge as a leading issue in medicine. Improving in-hospital management processes is thus the most pragmatic choice that the institutions can make now. But AI is not only a source of new kinds of errors: it does help to contain common risks in diagnosis, treatment, and prevention programs (5–7). Therefore, a constructive and pragmatic attitude is needed.
One of the biggest challenges in modern healthcare is linked to healthcare-acquired infections (HAI): multidrug-resistant bacteria are a public health threat, and HAI inflate in-hospital mortality, length of stays, direct costs of care, and disability-adjusted life years (8).
Ferorelli et al. evaluated how a new clinical risk management protocol impacted newborns, finding a reduction in umbilical venous catheter infections and an improvement in hospital stay lengths. On the other hand, exploring medical malpractice claims for HAI, Grassi, Grazzini et al. performed a cost-effectiveness analysis using epidemiological data, including experts in infection prevention and control sitting on the hospital decisional committee. They found that improving the management of medical malpractice claims can improve the economic outcomes, containing the double cost (implementing policies against HAI and paying for the related claims) that many hospitals must face because of a public health issue (Grassi, Grazzini et al.). De Micco, Grassi et al. stressed that new technologies have already been introduced in many hospitals, but regulations are still vague and insufficient, with the borders between the liability of the manufacturer/developer, the institution, and the user still ill-defined. Instead, Kameyama et al. evaluated the impact on the drug market of a Japanese risk management plan, finding that, when specific adverse reactions and drug therapeutic categories were evaluated, it succeeded in improving patient safety.
Moreover, our special issue hosted a systematic review on the potential role of AI systems to promptly detect adverse events (making correlations that a human mind could not make or would take a longer time to make), predict future incidents, and assess risks of sentinel events like in-hospital falls (De Micco, Di Palma et al.). Regarding sentinel events, another contribution evaluated the actual impact of retained surgical foreign bodies impact, finding that, despite their health consequences, they are generally mild and have a 2-fold risk of criminal complaint, indicating that the patient blames specific professionals rather than the (impersonal) institution for the events (Grassi, Focardi et al.). Visci et al. focused on a topic of utmost relevance: can the knowledge on the causal processes related to medical errors developed by the experts in legal medicine be used to improve clinical services? Their observational study concluded that structured forensic consultation services should be incorporated into clinical practice, being able to quickly and professionally address hot topics like capacity for consent that are frequent issues that can “paralyze” hospital activities (Visci et al.). Refolo et al. analyzed another face of the term “risk”: the risk of aggressive behavior based on genetic predisposition. Aggressivity is an issue of public interest both in hospital and common social contexts, and the potential role of genes in risk profiling has a potential dual use that should lead to strong ethical barriers (Refolo et al.). Finally, Aurilio et al. discussed the birth-related long bone fractures in healthy newborns—an example of an adverse event that can be both due to unavoidable factors and to human errors, with the compliance with best practices and prevention policies being the cornerstone of medico-legal defense.
In conclusion, in medicine, there is no such thing as a rigid dichotomic difference between errors and proper conduct, but there is a risk density entangled with medical services that can be addressed/neglected, inflated/deflated, or defined/left unknown. Hunting for manipulable variables in the unknown and improving the management of the risks are the real keywords of the papers of this special issue. To err is not only human anymore, but only humans can envision the future in healthcare and tailor new strategies to make it safe for all stakeholders.
Author contributions
SG: Writing – review & editing, Writing – original draft. FA: Writing – original draft, Writing – review & editing. DF: Writing – original draft, Writing – review & editing. FD: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Kohn L, Corrigan J, Donaldson M. To Err Is Human. Washington, DC: National Academies Press (2000).
2. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science. (1974) 185:1124–31. doi: 10.1126/science.185.4157.1124
3. Wiegmann DA, Wood LJ, Cohen TN, Shappell SA. Understanding the “swiss cheese model” and its application to patient safety. J Patient Saf. (2022) 18:119–23. doi: 10.1097/PTS.0000000000000810
4. Kostick-Quenet KM, Gerke S. AI in the hands of imperfect users. NPJ Digit Med. (2022) 5:197. doi: 10.1038/s41746-022-00737-z
5. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. (2023) 14:8459–86. doi: 10.1007/s12652-021-03612-z
6. Damiani G, Altamura G, Zedda M, Nurchis MC, Aulino G, Heidar Alizadeh A, et al. Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review. BMJ Open. (2023) 13:e065301. doi: 10.1136/bmjopen-2022-065301
7. Olawade DB, Wada OJ, David-Olawade AC, Kunonga E, Abaire O, Ling J. Using artificial intelligence to improve public health: a narrative review. Front Public Health. (2023) 11:1196397. doi: 10.3389/fpubh.2023.1196397
Keywords: risk management, legal medicine, medical error, medical bias, medical malpractice
Citation: Grassi S, Ausania F, Ferorelli D and De Micco F (2025) Editorial: Errors and biases in modern healthcare: public health, medico-legal and risk management aspects. Front. Med. 12:1676522. doi: 10.3389/fmed.2025.1676522
Received: 30 July 2025; Accepted: 08 August 2025;
Published: 29 August 2025.
Edited and reviewed by: Beatriz S. Lima, Research Institute for Medicines (iMed.ULisboa), Portugal
Copyright © 2025 Grassi, Ausania, Ferorelli and De Micco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Simone Grassi, c2ltb25lLmdyYXNzaUB1bmlmaS5pdA==