Enhancing the Utility of Health Related Quality of Life (HRQoL) Assessment Tools in Abdominal Wall Hernia (AWH) Surgery Through Artificial Intelligence (AI): A Framework Proposal

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Abstract
Abdominal wall hernia (AWH) represents an increasingly prevalent and clinically significant condition, particularly in high-income health systems where surgical volumes for hernia repair continue to rise (1,2), (3). The evaluation of surgical success has gradually expanded beyond recurrence rates and operative morbidity to include outcomes that are more reflective of patient priorities, such as restoration of function and overall quality of life (QoL) (4,5). AWH exerts a broad and often underappreciated impact on patients' lives. Qualitative research has identified interconnected psychosocial, physical, and socioeconomic domains that are adversely affected (6), including body image (7), mental health (8), sexual and social relationships, and employment.In recognition of these complexities, patient-reported outcome measures (PROMs) have gained prominence as essential tools for capturing the subjective burden of AWH and evaluation of surgical intervention, particularly in Complex Abdominal Wall Reconstruction (CAWR) (9,10). Among these, health-related quality of life (HRQoL) assessment tools represent a subset, designed to assess broader impacts of health on physical, psychological, and social functioning. Several instruments have been developed or adapted to quantify domains such as pain, physical limitation, and well-being (4). However, despite increasing uptake in research settings, HRQoL instruments are inconsistently employed in clinical practice, and their interpretive value remains limited (11,12). The predominant approach involves scoring and aggregating responses into numerical indices, overlooking nuanced patient experiences, especially in those with complex biopsychosocial needs (13).Concurrently, artificial intelligence (AI) has demonstrated being a powerful tool in healthcare analytics, offering new capabilities in pattern recognition, data analysis, and predictive analytics. AI refers to digital solutions that simulate cognitive tasks typically requiring human reasoning, including classification, prediction, and pattern recognition. Within this field, machine learning (ML) trains mathematical models to learn from data or past experience in order to make decisions or predictions, while natural language processing (NLP) enables computer algorithms to interpret and analyse human language (14). In surgical disciplines, AI offers an opportunity to enhance diagnostic imaging, intraoperative decision support, and postoperative risk stratification (15). More recently, attention has turned to AI's role in the analysis of PROMs and HRQoL tools, particularly through NLP and ML (16). In fields such as oncology and orthopaedics, AI is being explored as a potential avenue to enable deeper exploration of patient-reported data, including through the use of free-text narratives and longitudinal symptom trajectories, offering insights that traditional statistical approaches often fail to capture (17,18). This presents a timely opportunity for hernia surgery.This paper is positioned as an "Opinion" article, offering a conceptual perspective on how AI could enhance the utility of HRQoL assessment tools in AWH surgery. While existing tools are valuable, their reliance on closed-ended, structured response formats limits their ability to reflect the complexity of patients' lived experiences, particularly in domains such as body image, mental health, and interpersonal relationships. This paper has three main aims: to argue current data collection methods in AWH HRQoL tools insufficiently capture the biopsychosocial dimensions of patient experience; to advocate for a shift toward AI-augmented interpretation, with a particular focus on NLP; and to propose a vision for future research and clinical integration, where HRQoL tools serve as dynamic instruments for personalised care.We support this argument by appraising commonly used AWH HRQoL tools and drawing on other specialties where AI has enriched the analysis of similar data. We conclude with a proposed framework for implementing these approaches.To support the argument that current HRQoL tools in AWH surgery inadequately capture the complexity of patient experience, we appraise data collection methods and response formats employed by commonly used instruments. In a separate literature review (currently in press), we identified six condition-specific HRQoL assessment tools in AWH populations: Carolinas Comfort Scale (CCS) (19), Hernia-Related Quality of Life Survey (HerQLes) (20), European Registry for Abdominal Wall Hernias QoL instrument (EuraHS-QoL) (21), Activities Assessment Scale (AAS) (22), Abdominal Hernia-Q (AHQ) (23), and Hernia-Related Quality of Life instrument (HERQL) (24).Our appraisal focuses on response structures (such as Likert scales and binary items) and whether open-ended responses are permitted. Rather than assessing psychometric validity, we examine how design shapes the type and quality of data collected, and the extent to which it permits expression of meaningful patient perspectives.The structured appraisal presented in Table 1 highlights a unifying feature across HRQoL assessment tools in AWH surgery: a reliance on fixed-response, closed-ended formats that generate exclusively quantitative data. While practical, they are constrained in their capacity to capture the complexity of individual patient experience (25).Quantitative HRQoL assessment tools assume that complex biopsychosocial phenomena can be reduced to single numerical scores (26). Pain is commonly measured on a 0-10 scale, with little or no ability to capture its temporal variability, emotional salience, or relationship to social withdrawal. Similarly, a patient who reports "moderate" difficulty with movement may do so because of mechanical restriction, anxiety about recurrence, or fear of judgment in public. These are qualitatively distinct and clinically relevant.The absence of open-ended input prevents patients from contextualising their responses (27)....

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