The Value of Surgical Data—Impact on the Future of the Surgical Field

Abstract

The combination of computing power, connectivity, and big data has been touted as the future of innovation in many fields, including medicine. There has been a groundswell of companies developing tools for improving patient care utilizing healthcare data, but procedural specialties, like surgery, have lagged behind in benefitting from data-based innovations, given the lack of data that is well structured. While many companies are attempting to innovate in the surgical field, some have encountered difficulties around collecting surgical data, given its complex nature. As there is no standardized way in which to interact with healthcare systems to purchase these data, the authors attempt to characterize the various ways in which surgical data are collected and shared. By surveying and conducting interviews with various surgical technology companies, at least 3 different methods to collect surgical data were identified. From this information, the authors conclude that an attempt to outline best practices should be undertaken that benefits all stakeholders.

Surgical data science – from concepts toward clinical translation

“…(goals 4.1/4.2) Clinical data is recognized as "the resource most central to healthcare progress" (Institute of Medicine (USA) Roundtable on Value & Science-Driven Health Care, 2010). What is needed is thus a cultural shift towards data acquisition, annotation and analysis within a well-defined data governance framework as a primary clinical task (August et al, 2021). The allocation of economic, infrastructural and personnel resources within hospitals for this appears as a nonnegotiable requirement for the purpose.…”

“…From an ethico-legal perspective, it is worth noting that companies commonly obtain surgical data either through contracts with individual consulting surgeons, licensing agreements with hospitals or in exchange for discounted pricing of their products. This current practice raises important issues regarding power imbalances and the democratization of data access ( August et al, 2021 ).…”

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

“…Additionally, it is imperative to ensure that AI-method research aligns with clinical priorities and objectives and to implement standardized data collection, annotation, and analysis within a robust data governance framework. [197] Notably, data simulation algorithms, including variational autoencoders, generative adversarial networks, and diffusion models, have shown feasibility and can be utilized for training models requiring extensive datasets. [198][199][200][201] This may be a crucial solution to address the scarcity of medical information datasets.…”

“…Additionally, it is imperative to ensure that AI‐method research aligns with clinical priorities and objectives and to implement standardized data collection, annotation, and analysis within a robust data governance framework. [ 197 ]…”

Real-time Augmented Reality—The Next Frontier for Ophthalmic Surgery

“…To take an example of an internal limiting membrane peel in vitreoretinal surgery, real-time digital enhancement of internal limiting membrane staining could decrease exposure to and toxicity of vital dyes, audiovisual feedback about the proximity of intraocular instruments to the neurosensory retina could decrease the risk of iatrogenic trauma, and sizing templates could guide the width of the peel. Given the routine use of operating microscopes in ophthalmic surgery, vast quantities of surgical videos could be used to train precise deep learning algorithms, although devising a system to share surgical data, particularly labor-intensive, expert-annotated data, is an important challenge that will need to be addressed …”