Clinical and operational insights from data-driven care pathway mapping: a systematic review

Background

Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The “as is” or “de facto” care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.

Methods

A systematic review was conducted of literature abstracting sequential patient care activities (“de facto” care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.

Results

254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.

Discussion

Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.

Conclusion

This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01756-2.

Measuring and evaluating standardization of scrub nurse instrument table setups: a multi-center study

Objectives

In-depth knowledge about surgical processes is a crucial prerequisite for future systems in operating rooms and the advancement of standards and patient safety in surgery. A holistic approach is required, but research in the field of surgical instrument tables, standardized instrument setups and involved personnel, such as nurses, is sparse in general. The goal of this study is to evaluate whether there is an existing standard within clinics for an instrument table setup. We also evaluate to which extent it is known to the personnel and whether it is accepted.

Materials and Methods

The study makes use of the Nosco Trainer, a scrub nurse training and simulation system developed to analyze various aspects of the workplace of scrub nurses. The system contains a virtual instrument table, which is used to perform and record instrument table setups. We introduce a metric which delivers a measurable score for the similarity of surgical instrument table setups. The study is complemented with a questionnaire covering related aspects.

Results

Fifteen scrub nurses of the Otolaryngology departments at three clinics in Germany and Switzerland performed a table setup for a Functional Endoscopic Sinus Surgery intervention and completed the questionnaire. The analysis of the developed metric with a leave one out cross-validation correctly allocated 14 of the 15 participants to their clinic.

Discussion

In contrast to the identified similarities of table setups within clinics with the collected data, only a third of the participants confirmed in the questionnaire that there is an existing table setup standard for Functional Endoscopic Sinus Surgery interventions in their facility, but almost three quarters would support a written standard and acknowledge its possible benefits for trainees and new entrants in the operating room.

Conclusions

The structured analysis of the surgical instrument table using a data-driven metric for comparison is a novel approach to gain deeper knowledge about intra-operative processes. The insights can contribute to patient safety by improving the workflow between surgeon and scrub nurse and also open the way for goal-oriented standardization.

Supplementary Information

The online version supplementary material available at 10.1007/s11548-021-02556-1.

Distance Measures for Surgical Process Models

Background: The development of new resources, such as surgical techniques and approaches, results in continuous modification of surgery. To assess these modifications, it is necessary to use measures that quantify the impact of resources on surgical processes.

Objectives: The objective of this work is to introduce and evaluate distance measurements that are able to represent differences in the courses of surgical interventions as processes.

Methods: Hence, we present four different distance measures for surgical processes: the Jaccard distance, Levenshtein distance, Adjacency distance, and Graph matching distance. These measures are formally introduced and evaluated by applying them to clinical data sets from laparoscopic training in pediatric surgery.

Results: We analyzed the distances of 450 surgical processes using these four measures with a focus on the difference in surgical processes performed by novices and by experienced surgeons. The Levenshtein and Adjacency distances were best suited to measure distances between surgical processes.

Conclusion: The measurement of distances between surgical processes is necessary to estimate the benefit of new surgical techniques and strategies.

Video-based detection of device interaction in the operating room

The establishment of modern workflow management technologies requires the integration of dated devices. The extraction of the essential device data and usage time spans is a central requirement for an integrated OR environment. Therefore, methods are required that extract such information from the output provided by older generation devices, namely video signals. We developed a four-level approach for video-based device information extraction. Usually, video streams contain all relevant patient data and device usage information. We propose an approach consisting of defining regions of interest, grabbing video signals, analyzing the signals and storing the data in a centralized and structured location. The analysis considers textual information and graphical visualization. A prototype of the analysis approach was implemented and applied to a neurosurgical case. An evaluation study was conducted to measure the performance of the approach on video recordings of real interventions. Three medical devices were considered: intraoperative ultrasound, neuro-navigation and microscope. Overall, recognition rates for device usage higher than 95% were obtained. The approach is not limited to a single surgical discipline and does not require modification of medical devices. Furthermore, the analysis of microscopic video streams expands the detectable aspects of the surgical workflow beyond the recognition of device usage.

Multi-perspective workflow modeling for online surgical situation models

INTRODUCTION

Surgical workflow management is expected to enable situation-aware adaptation and intelligent systems behavior in an integrated operating room (OR). The overall aim is to unburden the surgeon and OR staff from both manual maintenance and information seeking tasks. A major step toward intelligent systems behavior is a stable classification of the surgical situation from multiple perspectives based on performed low-level tasks.

MATERIAL AND METHODS

The present work proposes a method for the classification of surgical situations based on multi-perspective workflow modeling. A model network that interconnects different types of surgical process models is described. Various aspects of a surgical situation description were considered: low-level tasks, high-level tasks, patient status, and the use of medical devices. A study with sixty neurosurgical interventions was conducted to evaluate the performance of our approach and its robustness against incomplete workflow recognition input.

RESULTS

A correct classification rate of over 90% was measured for high-level tasks and patient status. The device usage models for navigation and neurophysiology classified over 95% of the situations correctly, whereas the ultrasound usage was more difficult to predict. Overall, the classification rate decreased with an increasing level of input distortion.

DISCUSSION

Autonomous adaptation of medical devices and intelligent systems behavior do not currently depend solely on low-level tasks. Instead, they require a more general type of understanding of the surgical condition. The integration of various surgical process models in a network provided a comprehensive representation of the interventions and allowed for the generation of extensive situation descriptions.

CONCLUSION

Multi-perspective surgical workflow modeling and online situation models will be a significant pre-requisite for reliable and intelligent systems behavior. Hence, they will contribute to a cooperative OR environment.

Vision-based online recognition of surgical activities

PURPOSE

Surgical processes are complex entities characterized by expressive models and data. Recognizable activities define each surgical process. The principal limitation of current vision-based recognition methods is inefficiency due to the large amount of information captured during a surgical procedure. To overcome this technical challenge, we introduce a surgical gesture recognition system using temperature-based recognition.

METHODS

An infrared thermal camera was combined with a hierarchical temporal memory and was used during surgical procedures. The recordings were analyzed for recognition of surgical activities. The image sequence information acquired included hand temperatures. This datum was analyzed to perform gesture extraction and recognition based on heat differences between the surgeon's warm hands and the colder background of the environment.

RESULTS

The system was validated by simulating a functional endoscopic sinus surgery, a common type of otolaryngologic surgery. The thermal camera was directed toward the hands of the surgeon while handling different instruments. The system achieved an online recognition accuracy of 96% with high precision and recall rates of approximately 60%.

CONCLUSION

Vision-based recognition methods are the current best practice approaches for monitoring surgical processes. Problems of information overflow and extended recognition times in vision-based approaches were overcome by changing the spectral range to infrared. This change enables the real-time recognition of surgical activities and provides online monitoring information to surgical assistance systems and workflow management systems.

Surgical process modelling: a review

PURPOSE

Surgery is continuously subject to technological and medical innovations that are transforming daily surgical routines. In order to gain a better understanding and description of surgeries, the field of surgical process modelling (SPM) has recently emerged. The challenge is to support surgery through the quantitative analysis and understanding of operating room activities. Related surgical process models can then be introduced into a new generation of computer-assisted surgery systems.

METHODS

In this paper, we present a review of the literature dealing with SPM. This methodological review was obtained from a search using Google Scholar on the specific keywords: "surgical process analysis", "surgical process model" and "surgical workflow analysis".

RESULTS

This paper gives an overview of current approaches in the field that study the procedural aspects of surgery. We propose a classification of the domain that helps to summarise and describe the most important components of each paper we have reviewed, i.e., acquisition, modelling, analysis, application and validation/evaluation. These five aspects are presented independently along with an exhaustive list of their possible instantiations taken from the studied publications.

CONCLUSION

This review allows a greater understanding of the SPM field to be gained and introduces future related prospects.

Requirements for the structured recording of surgical device data in the digital operating room

PURPOSE

Due to the increasing complexity of the surgical working environment, increasingly technical solutions must be found to help relieve the surgeon. This objective is supported by a structured storage concept for all relevant device data.

METHODS

In this work, we present a concept and prototype development of a storage system to address intraoperative medical data. The requirements of such a system are described, and solutions for data transfer, processing, and storage are presented. In a subsequent study, a prototype based on the presented concept is tested for correct and complete data transmission and storage and for the ability to record a complete neurosurgical intervention with low processing latencies. In the final section, several applications for the presented data recorder are shown.

RESULTS

The developed system based on the presented concept is able to store the generated data correctly, completely, and quickly enough even if much more data than expected are sent during a surgical intervention.

CONCLUSIONS

The Surgical Data Recorder supports automatic recognition of the interventional situation by providing a centralized data storage and access interface to the OR communication bus. In the future, further data acquisition technologies should be integrated. Therefore, additional interfaces must be developed. The data generated by these devices and technologies should also be stored in or referenced by the Surgical Data Recorder to support the analysis of the OR situation.

The impact of missing sensor information on surgical workflow management

OBJECTIVE

Sensor systems in the operating room may encounter intermittent data losses that reduce the performance of surgical workflow management systems (SWFMS). Sensor data loss could impact SWFMS-based decision support, device parameterization, and information presentation. The purpose of this study was to understand the robustness of surgical process models when sensor information is partially missing. SWFMS changes caused by wrong or no data from the sensor system which tracks the progress of a surgical intervention were tested.

MATERIALS AND METHODS

The individual surgical process models (iSPMs) from 100 different cataract procedures of 3 ophthalmologic surgeons were used to select a randomized subset and create a generalized surgical process model (gSPM). A disjoint subset was selected from the iSPMs and used to simulate the surgical process against the gSPM. The loss of sensor data was simulated by removing some information from one task in the iSPM. The effect of missing sensor data was measured using several metrics: (a) successful relocation of the path in the gSPM, (b) the number of steps to find the converging point, and (c) the perspective with the highest occurrence of unsuccessful path findings.

RESULTS

A gSPM built using 30% of the iSPMs successfully found the correct path in 90% of the cases. The most critical sensor data were the information regarding the instrument used by the surgeon.

CONCLUSION

We found that use of a gSPM to provide input data for a SWFMS is robust and can be accurate despite missing sensor data. A surgical workflow management system can provide the surgeon with workflow guidance in the OR for most cases. Sensor systems for surgical process tracking can be evaluated based on the stability and accuracy of functional and spatial operative results.

Intervention time prediction from surgical low-level tasks

OBJECTIVE

Effective time and resource management in the operating room requires process information concerning the surgical procedure being performed. A major parameter relevant to the intraoperative process is the remaining intervention time. The work presented here describes an approach for the prediction of the remaining intervention time based on surgical low-level tasks.

MATERIALS AND METHODS

A surgical process model optimized for time prediction was designed together with a prediction algorithm. The prediction accuracy was evaluated for two different neurosurgical interventions: discectomy and brain tumor resections. A repeated random sub-sampling validation study was conducted based on 20 recorded discectomies and 40 brain tumor resections.

RESULTS

The mean absolute error of the remaining intervention time predictions was 13 min 24s for discectomies and 29 min 20s for brain tumor removals. The error decreases as the intervention progresses.

DISCUSSION

The approach discussed allows for the on-line prediction of the remaining intervention time based on intraoperative information. The method is able to handle demanding and variable surgical procedures, such as brain tumor resections. A randomized study showed that prediction accuracies are reasonable for various clinical applications.

CONCLUSION

The predictions can be used by the OR staff, the technical infrastructure of the OR, and centralized management. The predictions also support intervention scheduling and resource management when resources are shared among different operating rooms, thereby reducing resource conflicts. The predictions could also contribute to the improvement of surgical workflow and patient care.