Value-based Healthcare: Can Artificial Intelligence Provide Value in Orthopaedic Surgery?

Abstract

Artificial intelligence (AI), broadly defined as a “branch of computer science that simulates intelligent behavior in computers” [9], has expanded into the healthcare sector with the promise of enhanced predictive, diagnostic, and decision-making capabilities [11]. The accelerating allure of AI in health care has been fueled by growing datasets, algorithmic innovation, storage capacity, and the steep rise in affordable computational power [11]. But can AI-related applications provide value in orthopaedic surgery? Famed physicist, author, and cosmologist Stephen Hawking stated that AI will either be the best or worst thing to happen to humanity [3]. While we may be a long way off from a Hollywood-styled “rise of the machines,” focusing our AI efforts on value may help us to transform orthopaedic health care. We believe value-oriented AI-related applications in orthopaedics might best focus on three domains: (1) Advanced data discovery and extraction, (2) improved diagnostics and prediction, and (3) enhanced clinical and decision support. Data Discovery and Extraction Deep-learning predictive models use advanced algorithms and multi-layered artificial neural networks to recognize, learn, and improve upon patterns and correlations from massive datasets [1]. Each successive layer builds upon the output from the previous layer, and this multi-layered structure allows for powerful conclusions based on unstructured data. The complex mapping of individual linguistic elements and idiomatic phrases for natural language processing tools used for physician dictation is one example. These “neural network” models can also take raw, unorganized data from electronic health records (EHRs) and stratify patients by risk potential, or predict adverse events such as in-hospital mortality, sepsis, multi-organ failure, unplanned readmissions, or prolonged length of stay [13]. While these applications offer clinical benefits in terms of forecasting the risk of adverse events for acutely ill and surgical patients, future studies should examine whether AI can project pain, function, and health-related quality of life based on deep learning from big clinical data. Operational factors like clinical work flow, resource utilization, and costs of integrating such systems into electronic health platforms may also serve as fodder for examining the use of AI for data discovery. Diagnostics and Prediction When applied to digital-imaging data, machine and deep-learning AI approaches have demonstrated improved image acquisition and disease detection compared to conventional imaging for detecting long-bone and fragility fractures, differentiating between benign and malignant bone tumors, determining prognosis for patients with cancer [16], determining the risk of death after arthroplasty [2], and mapping disease progression for developmental dysplasia of the hip, or degenerative disease of the spine and lower extremities [1]. While many of these applications are still experimental [15], the growing evidence base for their technical and clinical efficacy suggests we are approaching a tipping point for more widespread implementation. Future studies should examine how these solutions can move the needle on process-level metrics such as minutes required for analysis and detection rate. These studies should also evaluate the impact on patient-focused outcomes and cost-effectiveness, with special consideration given to upfront costs and care delivery implications like clinical workflows and workforce utilization. Clinical Decision Support Clinicians and patients can use AI to engage in shared decision-making [14]. Clinicians can apply patient-reported outcomes (PROs), paired with demographic and clinical data, to machine-learning algorithms, which then provide patient-specific risk-benefit ratios for, as an example, the likelihood of achieving benefit following arthroplasty surgery [7, 14]. We have also seen machine-learning data applied to shared decision-making in kne