Video-based detection of device interaction in the operating room

Surgical data recording in the operating room: a systematic review of modalities and metrics

INTRODUCTION

Operating room recording, via video, audio and sensor-based recordings, is increasingly common. Yet, surgical data science is a new field without clear guidelines. The purpose of this study is to examine existing published studies of surgical recording modalities to determine which are available for use in the operating room, as a first step towards developing unified standards for this field.

METHODS

Medline, EMBASE, CENTRAL and PubMed databases were systematically searched for articles describing modalities of data collection in the operating room. Search terms included 'video-audio media', 'bio-sensing techniques', 'sound', 'movement', 'operating rooms' and others. Title, abstract and full-text screening were completed to identify relevant articles. Descriptive statistical analysis was performed for included studies.

RESULTS

From 3756 citations, 91 studies met inclusion criteria. These studies described 10 unique data-collection modalities for 17 different purposes in the operating room. Data modalities included video, audio, kinematic and eye-tracking among others. Data-collection purposes described included surgical trainee assessment, surgical error, surgical team communication and operating room efficiency.

CONCLUSION

Effective data collection and utilization in the operating room are imperative for the provision of superior surgical care. The future operating room landscape undoubtedly includes multiple modalities of data collection for a plethora of purposes. This review acts as a foundation for employing operating room data in a way that leads to meaningful benefit for patient care.

Surgical process modelling strategies: which method to choose for determining workflow?

The vital role of surgeries in healthcare requires a constant attention to improvement. Surgical process modelling is an innovative and rather recently introduced approach for tackling the issues in today's complex surgeries. This modelling field is very challenging and still under development, therefore, it is not always clear which modelling strategy would best fit the needs in which situations. The aim of this study was to provide a guide for matching the choice of modelling strategies for determining surgical workflows. In this work, the concepts associated with surgical process modelling are described, aiming to clarify them and to promote their use in future studies. The relationship of these concepts and the possible combinations of the suitable approaches for modelling strategies are elaborated and the criteria for opting for the proper modelling strategy are discussed.

Surgical data science: The new knowledge domain

Healthcare in general, and surgery/interventional care in particular, is evolving through rapid advances in technology and increasing complexity of care, with the goal of maximizing the quality and value of care. Whereas innovations in diagnostic and therapeutic technologies have driven past improvements in the quality of surgical care, future transformation in care will be enabled by data. Conventional methodologies, such as registry studies, are limited in their scope for discovery and research, extent and complexity of data, breadth of analytical techniques, and translation or integration of research findings into patient care. We foresee the emergence of surgical/interventional data science (SDS) as a key element to addressing these limitations and creating a sustainable path toward evidence-based improvement of interventional healthcare pathways. SDS will create tools to measure, model, and quantify the pathways or processes within the context of patient health states or outcomes and use information gained to inform healthcare decisions, guidelines, best practices, policy, and training, thereby improving the safety and quality of healthcare and its value. Data are pervasive throughout the surgical care pathway; thus, SDS can impact various aspects of care, including prevention, diagnosis, intervention, or postoperative recovery. The existing literature already provides preliminary results, suggesting how a data science approach to surgical decision-making could more accurately predict severe complications using complex data from preoperative, intraoperative, and postoperative contexts, how it could support intraoperative decision-making using both existing knowledge and continuous data streams throughout the surgical care pathway, and how it could enable effective collaboration between human care providers and intelligent technologies. In addition, SDS is poised to play a central role in surgical education, for example, through objective assessments, automated virtual coaching, and robot-assisted active learning of surgical skill. However, the potential for transforming surgical care and training through SDS may only be realized through a cultural shift that not only institutionalizes technology to seamlessly capture data but also assimilates individuals with expertise in data science into clinical research teams. Furthermore, collaboration with industry partners from the inception of the discovery process promotes optimal design of data products as well as their efficient translation and commercialization. As surgery continues to evolve through advances in technology that enhance delivery of care, SDS represents a new knowledge domain to engineer surgical care of the future.