[Intelligent operating room suite : From passive medical devices to the self-thinking cognitive surgical assistant]

Role of Intelligent Management Systems in Surgical Punctuality and Quality of Care

Objective

The main objective is to illustrate the role of intelligent management systems in surgical punctuality and quality of care.

Methods

72 registered nurses were selected from our operating room, and 180 patients who needed surgery were randomly divided into the control group and the observation group for satisfaction survey and satisfaction analysis.

Results

The correct rate of surgical clothing distribution and the qualified rate of clothing recovery were improved, and the punctuality rate of the operation was enhanced than before the implementation of the intelligent management system. The accurate positioning of surgical items and the accurate statistics of equipment use time were enhanced than before implementation. The error rate of surgical item preparation after implementation was lessened than before implementation. Both nursing satisfaction and patient satisfaction after implementation were increased than before implementation.

Conclusion

The intelligent management system improves the punctuality of surgery and the quality of care in the operating room.

[Image-based supportive measures for future application in surgery]

BACKGROUND

The development of assistive technologies will become of increasing importance in the coming years and not only in surgery. The comprehensive perception of the actual situation is the basis of every autonomous action. Different sensor systems can be used for this purpose, of which video-based systems have a special potential.

METHOD

Based on the available literature and on own research projects, central aspects of image-based support systems for surgery are presented. In this context, not only the potential but also the limitations of the methods are explained.

RESULTS

An established application is the phase detection of surgical interventions, for which surgical videos are analyzed using neural networks. Through a time-based and transformative analysis the results of the prediction could only recently be significantly improved. Robotic camera guidance systems will also use image data to autonomously navigate laparoscopes in the near future. The reliability of the systems needs to be adapted to the high requirements in surgery by means of additional information. A comparable multimodal approach has already been implemented for navigation and localization during laparoscopic procedures. For this purpose, video data are analyzed using various methods and these data are fused with other sensor modalities.

DISCUSSION

Image-based supportive methods are already available for various tasks and will become an important aspect for the surgery of the future; however, in order to be able to be reliably implemented for autonomous functions, they must be embedded in multimodal approaches in the future in order to provide the necessary security.

Machine Learning for Surgical Phase Recognition: A Systematic Review

OBJECTIVE

To provide an overview of ML models and data streams utilized for automated surgical phase recognition.

BACKGROUND

Phase recognition identifies different steps and phases of an operation. ML is an evolving technology that allows analysis and interpretation of huge data sets. Automation of phase recognition based on data inputs is essential for optimization of workflow, surgical training, intraoperative assistance, patient safety, and efficiency.

METHODS

A systematic review was performed according to the Cochrane recommendations and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Web of Science, IEEExplore, GoogleScholar, and CiteSeerX were searched. Literature describing phase recognition based on ML models and the capture of intraoperative signals during general surgery procedures was included.

RESULTS

A total of 2254 titles/abstracts were screened, and 35 full-texts were included. Most commonly used ML models were Hidden Markov Models and Artificial Neural Networks with a trend towards higher complexity over time. Most frequently used data types were feature learning from surgical videos and manual annotation of instrument use. Laparoscopic cholecystectomy was used most commonly, often achieving accuracy rates over 90%, though there was no consistent standardization of defined phases.

CONCLUSIONS

ML for surgical phase recognition can be performed with high accuracy, depending on the model, data type, and complexity of surgery. Different intraoperative data inputs such as video and instrument type can successfully be used. Most ML models still require significant amounts of manual expert annotations for training. The ML models may drive surgical workflow towards standardization, efficiency, and objectiveness to improve patient outcome in the future.

REGISTRATION PROSPERO

CRD42018108907.

Impact of Design on Medical Device Safety

The growing number of emerging medical technologies and sophistication of modern medical devices (MDs) that improve both survival and quality of life indexes are often challenged by alarming cases of vigilance data cover-up and lack of sufficient pre- and post-authorization controls. Combining Quality with Risk Management processes and implementing them as early as possible in the design of MDs has proven to be an effective strategy to minimize residual risk. This article aims to discuss how the design of MDs interacts with their safety profile and how this dipole of intended performance and safety may be supported by Human Factors Engineering (HFE) throughout the Total Product Life-Cycle (TPLC) of an MD in order to capitalize on medical technologies without exposing users and patients to unnecessary risks.

[Comprehensive system integration and networking in operating rooms]

BACKGROUND

A comprehensive surveillance and control system integrating all devices and functions is a precondition for realization of the operating room of the future.

STATE OF THE ART

Multiple proprietary integrated operation room systems are currently available with a central user interface; however, they only cover a relatively small part of all functionalities.

INNOVATIVE APPROACHES

Internationally, there are at least three different initiatives to promote a comprehensive systems integration and networking in the operating room: the Japanese smart cyber operating theater (SCOT), the American medical device plug-and-play interoperability program (MDPnP) and the German secure and dynamic networking in operating room and hospital (OR.NET) project supported by the Federal Ministry of Education and Research.

PRELIMINARY RESULTS

Within the framework of the internationally advanced OR.NET project, prototype solution approaches were realized, which make short-term and mid-term comprehensive data retrieval systems probable. An active and even autonomous control of the medical devices by the surveillance and control system (closed loop) is expected only in the long run due to strict regulatory barriers.

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.