Patient-Centered Surgical Care for Children in Low and Lower-Middle Income Countries (LMICs) - A Systematic Scoping Review of the Literature.

PURPOSE: Studies exploring patient-centered care (PCC) in pediatric surgery have been disproportionately concentrated in high-income countries. This review aims to characterize the adoption of key PCC domains in low and lower-middle income countries (LMICs). METHODS: Seven databases were searched from inception until January 2023 to retrieve relevant articles in pediatric surgery in LMICs. We focused on six key PCC domains: patient-reported outcomes (PROs), patient-reported experiences (PREs), shared decision-making (SDM), patient/parent education, patient/parent satisfaction, and informed consent. RESULTS: Of 8050 studies screened, 230 underwent full-text review, and 48 were finally included. Most were single-center (87.5%), cross-sectional studies (41.7%) from the South-East Asian (35.4%) and Eastern Mediterranean regions (33.3%). Studies most frequently focused on postoperative care (45.8%) in pediatric general surgery (18.8%), and included 1-3 PCC domains. PREs (n = 30), PROs (n = 16) and patient/parent satisfaction (n = 16) were most common. Informed consent (n = 2) and SDM (n = 1) were least studied. Only 13 studies directly elicited children's perspectives. Despite all studies originating in LMICs, 25% of first and 17.8% of senior authors lacked LMIC affiliations. CONCLUSION: The adoption of PCC in LMICs appears limited, focusing predominantly on PROs and PREs. Other domains such as informed consent and SDM are rarely addressed, and the voice of children and young people is rarely heard in their care. Opportunities to enhance PCC in LMICs abound, with the potential to improve the surgical care of children in resource-limited settings. LEVEL OF EVIDENCE: III.

The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review.

BACKGROUND: Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. METHODS: Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. RESULTS: Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. CONCLUSIONS: While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. LEVEL OF EVIDENCE: 2A.