Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review

Systematic review of the development and effectiveness of digital health information interventions, compared with usual care, in supporting patient preparation for paediatric hospital care, and the impact on their health outcomes

Background and aim

Elective surgery can be overwhelming for children, leading to pre-operative anxiety, which is associated with adverse clinical and behavioural outcomes. Evidence shows that paediatric preparation digital health interventions (DHIs) can contribute to reduced pre-operative anxiety and negative behavioural changes. However, this evidence does not consider their design and development in the context of behavioural science. This systematic review used the Theoretical Domains Framework (TDF) to evaluate the design and development of DHIs used to support children up to 14 years of age and their parents, prepare for hospital procedures, and determine any correlation to health outcomes. It also considered whether any behavioural frameworks and co-production were utilised in their design.

Methods

A search of the MEDLINE, EMBASE, PsycINFO, and HMIC databases was carried out, looking for original, empirical research using digital paediatric preparation technologies to reduce pre-operative anxiety and behavioural changes. Limitations for the period (2000-2022), English language, and age applied.

Results

Seventeen studies were included, sixteen randomised control trials and one before and after evaluation study. The results suggest that paediatric preparation DHIs that score highly against the TDF are (1) associated with improved health outcomes, (2) incorporate the use of co-production and behavioural science in their design, (3) are interactive, and (4) are used at home in advance of the planned procedure.

Conclusion

Paediatric preparation DHIs that are co-produced and designed in the context of behavioural science are associated with reduced pre-operative anxiety and improved health outcomes and may be more cost-effective than other interventions.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier: CRD42022274182.

Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns

Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area. IMPACT: Only a few publications on the use of AI in NEC diagnosis are available although they offer some evidence that AI may be helpful in NEC diagnosis. AI requires large, multicentre, and multimodal datasets of high quality for model training and testing. Published results in the literature are based on data from single institutions and, as such, have limited generalisability. Large multicentre studies evaluating broad datasets are needed to evaluate the true potential of AI in diagnosing NEC in a clinical setting.

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI's performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI's ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians' intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare

Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.

Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors

Pediatric patients, particularly in neonatal and pediatric intensive care units (NICUs and PICUs), are typically at an increased risk of fatal decompensation. That being said, any delay in treatment or minor errors in medication dosage can overcomplicate patient health. Under such an environment, clinicians are expected to quickly and effectively comprehend large volumes of medical information to diagnose and develop a treatment plan for any baby. The integration of Artificial Intelligence (AI) into the clinical workflow can be a potential solution to safeguard pediatric patients and augment the quality of care. However, before making AI an integral part of pediatric care, it is essential to evaluate the technology from a human factors perspective, ensuring its readiness (technology readiness level) and ecological validity. Addressing AI accountability is also critical to safeguarding clinicians and improving AI acceptance in the clinical workflow. This article summarizes the application of AI in NICU/PICU and consecutively identifies the existing flaws in AI (from clinicians’ standpoint), and proposes related recommendations, which, if addressed, can improve AIs’ readiness for a real clinical environment.