A comparison between a maximum care university hospital and an outpatient clinic - potential for optimization in arthroscopic workflows?

BACKGROUND: Due to the growing economic pressure, there is an increasing interest in the optimization of operational processes within surgical operating rooms (ORs). Surgical departments are frequently dealing with limited resources, complex processes with unexpected events as well as constantly changing conditions. In order to use available resources efficiently, existing workflows and processes have to be analyzed and optimized continuously. Structural and procedural changes without prior data-driven analyses may impair the performance of the OR team and the overall efficiency of the department. The aim of this study is to develop an adaptable software toolset for surgical workflow analysis and perioperative process optimization in arthroscopic surgery. METHODS: In this study, the perioperative processes of arthroscopic interventions have been recorded and analyzed subsequently. A total of 53 arthroscopic operations were recorded at a maximum care university hospital (UH) and 66 arthroscopic operations were acquired at a special outpatient clinic (OC). The recording includes regular perioperative processes (i.a. patient positioning, skin incision, application of wound dressing) and disruptive influences on these processes (e.g. telephone calls, missing or defective instruments, etc.). For this purpose, a software tool was developed ('s.w.an Suite Arthroscopic toolset'). Based on the data obtained, the processes of the maximum care provider and the special outpatient clinic have been analyzed in terms of performance measures (e.g. Closure-To-Incision-Time), efficiency (e.g. activity duration, OR resource utilization) as well as intra-process disturbances and then compared to one another. RESULTS: Despite many similar processes, the results revealed considerable differences in performance indices. The OC required significantly less time than UH for surgical preoperative (UH: 30:47 min, OC: 26:01 min) and postoperative phase (UH: 15:04 min, OC: 9:56 min) as well as changeover time (UH: 32:33 min, OC: 6:02 min). In addition, these phases result in the Closure-to-Incision-Time, which lasted longer at the UH (UH: 80:01 min, OC: 41:12 min). CONCLUSION: The perioperative process organization, team collaboration, and the avoidance of disruptive factors had a considerable influence on the progress of the surgeries. Furthermore, differences in terms of staffing and spatial capacities could be identified. Based on the acquired process data (such as the duration for different surgical steps or the number of interfering events) and the comparison of different arthroscopic departments, approaches for perioperative process optimization to decrease the time of work steps and reduce disruptive influences were identified.

Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.

[BIOPASS hybrid navigation for endoscopic sinus surgery - an assistance system]. / BIOPASS-Hybrid-Navigation für die endoskopische Nasennebenhöhlenchirurgie ­ ein Assistenzsystem.

Previous navigation systems can determine the position of the "tracked" surgical instrument in CT images in the context of functional endoscopic sinus surgery (FESS), but do not provide any assistance directly in the video endoscopic image of the surgeon. Developing this direct assistance for intraoperative orientation and risk reduction was the goal of the BIOPASS project (Bild Ontologie und prozessgestütztes Assistenzsystem). The Project pursues the development of a novel navigation system for FESS without markers. BIOPASS describes a hybrid system that integrates various sensor data and makes it available. The goal is to abandon tracking and exclusively provide navigation information directly in the video image. This paper describes the first step of the development by collecting and structuring the surgical phases (workflows), the video endoscopic landmarks and a first clinical evaluation of the model version. The results provide the important basis and platform for the next step of the project.

Alternative intraoperative optical imaging modalities for fluorescence angiography in gastrointestinal surgery: spectral imaging and imaging photoplethysmography.

INTRODUCTION: Intraoperative near-infrared fluorescence angiography with indocyanine green (ICG-FA) is a well-established modality in gastrointestinal surgery. Its main drawback is the application of a fluorescent agent with possible side effects for patients. The goal of this review paper is the presentation of alternative, non-invasive optical imaging methods and their comparison with ICG-FA. MATERIAL AND METHODS: The principles of ICG-FA, spectral imaging, imaging photoplethysmography (iPPG), and their applications in gastrointestinal surgery are described based on selected published works. RESULTS: The main applications of the three modalities are the evaluation of tissue perfusion, the identification of risk structures, and tissue segmentation or classification. While the ICG-FA images are mainly evaluated visually, leading to subjective interpretations, quantitative physiological parameters and tissue segmentation are provided in spectral imaging and iPPG. The combination of ICG-FA and spectral imaging is a promising method. CONCLUSIONS: Non-invasive spectral imaging and iPPG have shown promising results in gastrointestinal surgery. They can overcome the main drawbacks of ICG-FA, i.e. the use of contrast agents, the lack of quantitative analysis, repeatability, and a difficult standardization of the acquisition. Further technical improvements and clinical evaluations are necessary to establish them in daily clinical routine.

Ontology-based surgical workflow recognition and prediction.

BACKGROUND: Surgical context-aware systems can adapt to the current situation in the operating room and thus provide computer-aided assistance functionalities and intraoperative decision-support. To interact with the surgical team perceptively and assist the surgical process, the system needs to monitor the intraoperative activities, understand the current situation in the operating room at any time, and anticipate the following possible situations. METHODS: A structured representation of surgical process knowledge is a prerequisite for any applications in the intelligent operating room. For this purpose, a surgical process ontology, which is formally based on standard medical terminology (SNOMED CT) and an upper-level ontology (GFO), was developed and instantiated for a neurosurgical use case. A new ontology-based surgical workflow recognition and a novel prediction method are presented utilizing ontological reasoning, abstraction, and explication. This way, a surgical situation representation with combined phase, high-level task, and low-level task recognition and prediction was realized based on the currently used instrument as the only input information. RESULTS: The ontology-based approach performed efficiently, and decent accuracy was achieved for situation recognition and prediction. Especially during situation recognition, the missing sensor information were reasoned based on the situation representation provided by the process ontology, which resulted in improved recognition results compared to the state-of-the-art. CONCLUSIONS: In this work, a reference ontology was developed, which provides workflow support and a knowledge base for further applications in the intelligent operating room, for instance, context-aware medical device orchestration, (semi-) automatic documentation, and surgical simulation, education, and training.

Analysis of the paraspinal muscle morphology of the lumbar spine using a convolutional neural network (CNN).

PURPOSE: This single-center study aimed to develop a convolutional neural network to segment multiple consecutive axial magnetic resonance imaging (MRI) slices of the lumbar spinal muscles of patients with lower back pain and automatically classify fatty muscle degeneration. METHODS: We developed a fully connected deep convolutional neural network (CNN) with a pre-trained U-Net model trained on a dataset of 3,650 axial T2-weighted MRI images from 100 patients with lower back pain. We included all qualities of MRI; the exclusion criteria were fractures, tumors, infection, or spine implants. The training was performed using k-fold cross-validation (k = 10), and performance was evaluated using the dice similarity coefficient (DSC) and cross-sectional area error (CSA error). For clinical correlation, we used a simplified Goutallier classification (SGC) system with three classes. RESULTS: The mean DSC was high for overall muscle (0.91) and muscle tissue segmentation (0.83) but showed deficiencies in fatty tissue segmentation (0.51). The CSA error was small for the overall muscle area of 8.42%, and fatty tissue segmentation showed a high mean CSA error of 40.74%. The SGC classification was correctly predicted in 75% of the patients. CONCLUSION: Our fully connected CNN segmented overall muscle and muscle tissue with high precision and recall, as well as good DSC values. The mean predicted SGC values of all available patient axial slices showed promising results. With an overall Error of 25%, further development is needed for clinical implementation. Larger datasets and training of other model architectures are required to segment fatty tissue more accurately.

Dysphagia, voice problems, and pain in head and neck cancer patients.

PURPOSE: Head and neck cancer (HNC) and its treatment can leave devastating side effects with a relevant impact on physical and emotional quality of life (QoL) of HNC patients. The objectives were to examine the amount of dysphagia, voice problems, and pain in HNC patients, the impact of sociodemographic, behavioral, and clinical factors on these symptoms, the psychometric properties of the EAT-10, and the relationship between these symptoms and QoL variables. METHODS: HNC patients attending for regular follow-up from 07/2013 to 09/2019 completed questionnaires (Eating Assessment Tool-10 (EAT-10); questions from the EORTC QLQ-C30 and EORTC H&N35) on dysphagia, voice problems, pain, fatigue, and QoL collected with the software OncoFunction. Associations between prognostic factors and symptoms were tested with analyses of variance (ANOVAs). Associations between the symptom scales and QoL variables were expressed with Pearson correlations. RESULTS: Of 689 patients, 54.9% suffered from dysphagia, the EAT-10 proved to be a reliable measure. The mean voice score was 37.6 (± 33.9) [range 0-100], the mean pain score 1.98 (± 2.24) [range 0-10]. Trimodality treatment was associated with the highest dysphagia scores. Dysphagia, voice problems, and pain significantly correlated with each other, the highest association was found for dysphagia and pain (r = 0.51). QoL was strongly correlated with dysphagia and pain (r = - 0.39 and r = - 0.40, respectively), while the association with voice problems was weaker (r = - 0.28). CONCLUSION: Dysphagia is an important symptom in HNC patients greatly affecting patients' QoL and significantly correlating with voice problems and pain.

[Evaluation of an integrated OR based on open standards in Cochlea Implant surgery]. / Evaluation eines integrierten OP-Saals auf Basis offener Standards in der Cochlea-Implantat-Chirurgie.

BACKGROUND: Digitalization in surgery makes it necessary to develop modern surgical concepts. New approaches to system networking with integration and open standardized communication of all medical devices are being pursued. METHODS: At the University Hospital Leipzig, a demonstration of the integrated OR was carried out together with the Innovation Center for Computer Assisted Surgery (ICCAS) using the example of a cochlea implantation. The preoperative management, technical preparation, surgical procedure and postoperative documentation by a total of n = 30 study participants (2 expert groups) were evaluated. In addition to the collection of objective parameters, qualitative questionnaires and quantitative, interval-scaled questions were used. RESULTS: Preoperatively, the digital presentation of the patient's clinical data was considered as helpful by both groups (group 1: median = 5, group 2: median = 4). This also applies to the personalized OR settings, the intraoperative display options and the dynamic, surgeon-centered visualization (median = 4). Similar positive conclusions were drawn for postoperative documentation and postoperative follow-up (median = 4). A significant difference in the final evaluation of the integrated surgical concept between the two expert groups could not be determined (p > 0.05). CONCLUSIONS: The positive study results show that the theoretical idea of system networking based on open standards can be successfully implemented in practice using the example of a cochlea implantation. Thus, the intelligent "operating room of the future" no longer seems to be a fictitious idea, but a realistic image of modern surgical medicine.

Surgical workflow simulation for the design and assessment of operating room setups in orthopedic surgery.

BACKGROUND: The design and internal layout of modern operating rooms (OR) are influencing the surgical team's collaboration and communication, ergonomics, as well as intraoperative hygiene substantially. Yet, there is no objective method for the assessment and design of operating room setups for different surgical disciplines and intervention types available. The aim of this work is to establish an improved OR setup for common procedures in arthroplasty. METHODS: With the help of computer simulation, a method for the design and assessment of enhanced OR setups was developed. New OR setups were designed, analyzed in a computer simulation environment and evaluated in the actual intraoperative setting. Thereby, a 3D graphical simulation representation enabled the strong involvement of clinical stakeholders in all phases of the design and decision-making process of the new setup alternatives. RESULTS: The implementation of improved OR setups reduces the instrument handover time between the surgeon and the scrub nurse, the travel paths of the OR team as well as shortens the procedure duration. Additionally, the ergonomics of the OR staff were improved. CONCLUSION: The developed simulation method was evaluated in the actual intraoperative setting and proved its benefit for the design and optimization of OR setups for different surgical intervention types. As a clinical result, enhanced setups for total knee arthroplasty and total hip arthroplasty surgeries were established in daily clinical routine and the OR efficiency was improved.

Evaluation of hyperspectral imaging (HSI) for the measurement of ischemic conditioning effects of the gastric conduit during esophagectomy.

BACKGROUND: Hyperspectral imaging (HSI) is a relatively new method used in image-guided and precision surgery, which has shown promising results for characterization of tissues and assessment of physiologic tissue parameters. Previous methods used for analysis of preconditioning concepts in patients and animal models have shown several limitations of application. The aim of this study was to evaluate HSI for the measurement of ischemic conditioning effects during esophagectomy. METHODS: Intraoperative hyperspectral images of the gastric tube through the mini-thoracotomy were recorded from n = 22 patients, 14 of whom underwent laparoscopic gastrolysis and ischemic conditioning of the stomach with two-step transthoracic esophagectomy and gastric pull-up with intrathoracic anastomosis after 3-7 days. The tip of the gastric tube (later esophagogastric anastomosis) was measured with HSI. Analysis software provides a RGB image and 4 false color images representing physiologic parameters of the recorded tissue area intraoperatively. These parameters contain tissue oxygenation (StO2), perfusion-(NIR Perfusion Index), organ hemoglobin (OHI), and tissue water index (TWI). RESULTS: Intraoperative HSI of the gastric conduit was possible in all patients and did not prolong the regular operative procedure due to its quick applicability. In particular, the tissue oxygenation of the gastric conduit was significantly higher in patients who underwent ischemic conditioning ([Formula: see text] = 78%; [Formula: see text] = 66%; p = 0.03). CONCLUSIONS: HSI is suitable for contact-free, non-invasive, and intraoperative evaluation of physiological tissue parameters within gastric conduits. Therefore, HSI is a valuable method for evaluating ischemic conditioning effects and may contribute to reduce anastomotic complications. Additional studies are needed to establish normal values and thresholds of the presented parameters for the gastric conduit anastomotic site.

The Intelligent ENT Operating Room of the Future. / Der intelligente HNO-Operationssaal der Zukunft.

The increasing plurality and complexity of technical assistance systems pose a challenge for clinically active physicians. Particularly in the operating theater, there is a growing need to integrate medical systems and software solutions into a holistic clinical infrastructure. The primary goal of building a "digital (ENT) operating room of the future" is not just the pure technical improvement of the individual computer-aided equipment and instruments, but rather their dynamic networking and system integration in an open modular system. Promising scientific projects address the question of how to improve the quality, safety and user-friendliness of technical systems in the health care system of the 21st century. The work on SCOT, MD PnP and OR.NET show the various components that make the vision of the ENT operating room of the future tangible and realistic in the overall context.

Enabling artificial intelligence in high acuity medical environments.

Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as increased efficiency. Yet, only very few applications have been reported because of the limited accessibility of device data due to the lack of adoption of open standards, and the complexity of regulatory/approval requirements for AI-based systems. The fragmentation of data, still being stored in isolated silos, results in limited accessibility for AI in healthcare and machine learning is complicated by the loss of semantics in data conversions. We outline a reference model that addresses the requirements of innovative AI-based research systems as well as the clinical reality. The integration of networked medical devices and Clinical Repositories based on open standards, such as IEEE 11073 SDC and HL7 FHIR, will foster novel assistance and decision support. The reference model will make point-of-care device data available for AI-based approaches. Semantic interoperability between Clinical and Research Repositories will allow correlating patient data, device data, and the patient outcome. Thus, complete workflows in high acuity environments can be analysed. Open semantic interoperability will enable the improvement of patient outcome and the increase of efficiency on a large scale and across clinical applications.

Monitoring of microvascular free flaps following oropharyngeal reconstruction using infrared thermography: first clinical experiences.

The aim of this study is to investigate static and dynamic infrared (IR) thermography for intra- and postoperative free-flap monitoring following oropharyngeal reconstruction. Sixteen patients with oropharyngeal reconstruction by free radial forearm flap were included in this prospective, clinical study (05/2013-08/2014). Prior ("intraop_pre") and following ("intraop_post") completion of the microvascular anastomoses, IR thermography was performed for intraoperative flap monitoring. Further IR images were acquired one day ("postop_1") and 10 days ("postop_10") after surgery for postoperative flap monitoring. Of the 16, 15 transferred free radial forearm flaps did not show any perfusion failure. A significant decreasing mean temperature difference (∆T: temperature difference between the flap surface and the surrounding tissue in Kelvin) was measured at all investigation points in comparison with the temperature difference at "intraop_pre" (mean values on all patients: ∆T intraop_pre = -2.64 K; ∆T intraop_post = -1.22 K, p < 0.0015; ∆T postop_1 = -0.54 K, p < 0.0001; ∆T postop_10 = -0.58 K, p < 0.0001). Intraoperative dynamic IR thermography showed typical pattern of non-pathological rewarming due to re-established flap perfusion after completion of the microvascular anastomoses. Static and dynamic IR thermography is a promising, objective method for intraoperative and postoperative monitoring of free-flap reconstructions in head and neck surgery and to detect perfusion failure, before macroscopic changes in the tissue surface are obvious. A lack of significant decrease of the temperature difference compared to surrounding tissue following completion of microvascular anastomoses and an atypical rewarming following a thermal challenge are suggestive of flap perfusion failure.

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.

Predicting treatment process steps from events.

MOTIVATION: The primary economy-driven documentation of patient-specific information in clinical information systems leads to drawbacks in the use of these systems in daily clinical routine. Missing meta-data regarding underlying clinical workflows within the stored information is crucial for intelligent support systems. Unfortunately, there is still a lack of primary clinical needs-driven electronic patient documentation. Hence, physicians and surgeons must search hundreds of documents to find necessary patient data rather than accessing relevant information directly from the current process step. In this work, a completely new approach has been developed to enrich the existing information in clinical information systems with additional meta-data, such as the actual treatment phase from which the information entity originates. METHODS: Stochastic models based on Hidden Markov Models (HMMs) are used to create a mathematical representation of the underlying clinical workflow. These models are created from real-world anonymized patient data and are tailored to therapy processes for patients with head and neck cancer. Additionally, two methodologies to extend the models to improve the workflow recognition rates are presented in this work. RESULTS: A leave-one-out cross validation study was performed and achieved promising recognition rates of up to 90% with a standard deviation of 6.4%. CONCLUSIONS: The method presented in this paper demonstrates the feasibility of predicting clinical workflow steps from patient-specific information as the basis for clinical workflow support, as well as for the analysis and improvement of clinical pathways.

Sensor-based surgical activity recognition in unconstrained environments.

INTRODUCTION: Automatic surgical activity recognition in the operating room (OR) is mandatory to enable assistive surgical systems to manage the information presented to the surgical team. Therefore the purpose of our study was to develop and evaluate an activity recognition model. MATERIAL AND METHODS: The system was conceived as a hierarchical recognition model which separated the recognition task into activity aspects. The concept used radio frequency identification (RFID) for instrument recognition and accelerometers to infer the performed surgical action. Activity recognition was done by combining intermediate results of the aspect recognition. A basic scheme of signal feature generation, clustering and sequence learning was replicated in all recognition subsystems. Hidden Markov models (HMM) were used to generate probability distributions over aspects and activities. Simulated functional endoscopic sinus surgeries (FESS) were used to evaluate the system. RESULTS AND DISCUSSION: The system was able to detect surgical activities with an accuracy of 95%. Instrument recognition performed best with 99% accuracy. Action recognition showed lower accuracies with 81% due to the high variability of surgical motions. All stages of the recognition scheme were evaluated. The model allows distinguishing several surgical activities in an unconstrained surgical environment. Future improvements could push activity recognition even further.

The Emergency Medical Team Operating System - a vision for field hospital data management in following the concepts of predictive, preventive, and personalized medicine.

In times where sudden-onset disasters (SODs) present challenges to global health systems, the integration of predictive, preventive, and personalized medicine (PPPM / 3PM) into emergency medical responses has manifested as a critical necessity. We introduce a modern electronic patient record system designed specifically for emergency medical teams (EMTs), which will serve as a novel approach in how digital healthcare management can be optimized in crisis situations. This research is based on the principle that advanced information technology (IT) systems are key to transforming humanitarian aid by offering predictive insights, preventive strategies, and personalized care in disaster scenarios. We aim to address the critical gaps in current emergency medical response strategies, particularly in the context of SODs. Building upon a collaborative effort with European emergency medical teams, we have developed a comprehensive and scalable electronic patient record system. It not only enhances patient management during emergencies but also enables predictive analytics to anticipate patient needs, preventive guidelines to reduce the impact of potential health threats, and personalized treatment plans for the individual needs of patients. Furthermore, our study examines the possibilities of adopting PPPM-oriented IT solutions in disaster relief. By integrating predictive models for patient triage, preventive measures to mitigate health risks, and personalized care protocols, potential improvements to patient health or work efficiency could be established. This system was evaluated with clinical experts and shall be used to establish digital solutions and new forms of assistance for humanitarian aid in the future. In conclusion, to really achieve PPPM-related efforts more investment will need to be put into research and development of electronic patient records as the foundation as well as into the clinical processes along all pathways of stakeholders in disaster medicine.

Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications.

BACKGROUND: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer. METHODS: We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngeal cancer cases from the simulated population and deceased persons, stratified by the real-world age and sex distributions at diagnosis. RESULTS: A module with age- and stage-specific treatment and prognosis for laryngeal cancer was successfully implemented. The synthesized population reflects RW prevalence well, extracting a cohort of 50,000 laryngeal cancer patients. Descriptive data on stage-specific and 5-year overall survival were in accordance with published data. CONCLUSIONS: We developed a large cohort of realistic synthetic laryngeal cancer cases with Synthea. Such data can be shared and published open source without data protection issues.

TraumaFlow-development of a workflow-based clinical decision support system for the management of severe trauma cases.

PURPOSE: The treatment of severely injured patients in the resuscitation room of an emergency department requires numerous critical decisions, often under immense time pressure, which places very high demands on the facility and the interdisciplinary team. Computer-based cognitive aids are a valuable tool, especially in education and training of medical professionals. For the management of polytrauma cases, TraumaFlow, a workflow management-based clinical decision support system, was developed. The system supports the registration and coordination of activities in the resuscitation room and actively recommends diagnosis and treatment actions. METHODS: Based on medical guidelines, a resuscitation room algorithm was developed according to the cABCDE scheme. The algorithm was then modeled using the process description language BPMN 2.0 and implemented in a workflow management system. In addition, a web-based user interface that provides assistance functions was developed. An evaluation study was conducted with 11 final-year medical students and three residents to assess the applicability of TraumaFlow in a case-based training scenario. RESULTS: TraumaFlow significantly improved guideline-based decision-making, provided more complete therapy, and reduced treatment errors. The system was shown to be beneficial not only for the education of low- and medium-experienced users but also for the training of highly experienced physicians. 92% of the participants felt more confident with computer-aided decision support and considered TraumaFlow useful for the training of polytrauma treatment. In addition, 62% acknowledged a higher training effect. CONCLUSION: TraumaFlow enables real-time decision support for the treatment of polytrauma patients. It improves guideline-based decision-making in complex and critical situations and reduces treatment errors. Supporting functions, such as the automatic treatment documentation and the calculation of medical scores, enable the trauma team to focus on the primary task. TraumaFlow was developed to support the training of medical students and experienced professionals. Each training session is documented and can be objectively and qualitatively evaluated.