Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review
Background: Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/12/5/549 |
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| author | Michela Ferrara Giuseppe Bertozzi Nicola Di Fazio Isabella Aquila Aldo Di Fazio Aniello Maiese Gianpietro Volonnino Paola Frati Raffaele La Russa |
| author_facet | Michela Ferrara Giuseppe Bertozzi Nicola Di Fazio Isabella Aquila Aldo Di Fazio Aniello Maiese Gianpietro Volonnino Paola Frati Raffaele La Russa |
| author_sort | Michela Ferrara |
| collection | DOAJ |
| description | Background: Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. Materials and Methods: On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. Results and Discussion: The studies included in this review allowed for the identification of three main “incident type” domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. Conclusions: This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification. |
| first_indexed | 2024-04-25T00:30:14Z |
| format | Article |
| id | doaj.art-945e1139f82a45438702051611f7611c |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-04-25T00:30:14Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-945e1139f82a45438702051611f7611c2024-03-12T16:44:46ZengMDPI AGHealthcare2227-90322024-02-0112554910.3390/healthcare12050549Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic ReviewMichela Ferrara0Giuseppe Bertozzi1Nicola Di Fazio2Isabella Aquila3Aldo Di Fazio4Aniello Maiese5Gianpietro Volonnino6Paola Frati7Raffaele La Russa8Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, ItalyComplex Intercompany Structure of Forensic Medicine, 85100 Potenza, ItalyDepartment of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, ItalyRegional Hospital “San Carlo”, 85100 Potenza, ItalyDepartment of Surgical Pathology, Medical, Molecular and Critical Area, Institute of Legal Medicine, University of Pisa, 56126 Pisa, ItalyDepartment of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Clinical Medicine, Public Health, Life and Environment Science, University of L’Aquila, 67100 L’Aquila, ItalyBackground: Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. Materials and Methods: On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. Results and Discussion: The studies included in this review allowed for the identification of three main “incident type” domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. Conclusions: This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.https://www.mdpi.com/2227-9032/12/5/549artificial intelligenceclinical risk managementpatient safetymachine learning |
| spellingShingle | Michela Ferrara Giuseppe Bertozzi Nicola Di Fazio Isabella Aquila Aldo Di Fazio Aniello Maiese Gianpietro Volonnino Paola Frati Raffaele La Russa Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review Healthcare artificial intelligence clinical risk management patient safety machine learning |
| title | Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review |
| title_full | Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review |
| title_fullStr | Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review |
| title_full_unstemmed | Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review |
| title_short | Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review |
| title_sort | risk management and patient safety in the artificial intelligence era a systematic review |
| topic | artificial intelligence clinical risk management patient safety machine learning |
| url | https://www.mdpi.com/2227-9032/12/5/549 |
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