Sensor-Based Discovery of Search and Palpation Modes in the Clinical Breast Examination.

PURPOSE: Successful implementation of precision education systems requires widespread adoption and seamless integration of new technologies with unique data streams that facilitate real-time performance feedback. This paper explores the use of sensor technology to quantify hands-on clinical skills. The goal is to shorten the learning curve through objective and actionable feedback. METHOD: A sensor-enabled clinical breast examination (CBE) simulator was used to capture force and video data from practicing clinicians (N = 152). Force-by-time markers from the sensor data and a machine learning algorithm were used to parse physicians' CBE performance into periods of search and palpation and then these were used to investigate distinguishing characteristics of successful versus unsuccessful attempts to identify masses in CBEs. RESULTS: Mastery performance from successful physicians showed stable levels of speed and force across the entire CBE and a 15% increase in force when in palpation mode compared with search mode. Unsuccessful physicians failed to search with sufficient force to detect deep masses ( F [5,146] = 4.24, P = .001). While similar proportions of male and female physicians reached the highest performance level, males used more force as noted by higher palpation to search force ratios ( t [63] = 2.52, P = .014). CONCLUSIONS: Sensor technology can serve as a useful pathway to assess hands-on clinical skills and provide data-driven feedback. When using a sensor-enabled simulator, the authors found specific haptic approaches that were associated with successful CBE outcomes. Given this study's findings, continued exploration of sensor technology in support of precision education for hands-on clinical skills is warranted.

Precision Education: The Future of Lifelong Learning in Medicine.

ABSTRACT: The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE).PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE "cycles" start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics , insights are generated to drive precision interventions . At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational levels, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture.The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.

Haptics: The Science of Touch As a Foundational Pathway to Precision Education and Assessment.

ABSTRACT: Clinical touch is the cornerstone of the doctor-patient relationship and can impact patient experience and outcomes. In the current era, driven by an ever-increasing infusion of point-of-care technologies, physical exam skills have become undervalued. Moreover, touch and hands-on skills have been difficult to teach due to inaccurate assessments and difficulty with learning transfer through observation. In this article, the authors argue that haptics, the science of touch, provides a unique opportunity to explore new pathways to facilitate touch training. Furthermore, haptics can dramatically increase the density of touch-based assessments without increasing human rater burden-essential for realizing precision assessment. The science of haptics is reviewed, including the benefits of using haptics-informed language for objective structured clinical examinations. The authors describe how haptic devices and haptic language have and can be used to facilitate learning, communication, documentation and a much-needed reinvigoration of physical examination, and touch excellence at the point of care. The synergy of haptic devices, artificial intelligence, and virtual reality environments are discussed. The authors conclude with challenges of scaling haptic technology in medical education, such as cost and translational needs, and opportunities to achieve wider adoption of this transformative approach to precision education.

Surgical Education.

This paper summarizes the proceedings of the joint European Surgical Association ESA/American Surgical Association symposium on Surgical Education that took place in Bordeaux, France, as part of the celebrations for 30 years of ESA scientific meetings. Three presentations on the use of quantitative metrics to understand technical decisions, coaching during training and beyond, and entrustable professional activities were presented by American Surgical Association members and discussed by ESA members in a symposium attended by members of both associations.

Surgical Instant Replay-A National Video-Based Performance Assessment Toolbox.

This article discusses the widespread implementation of surgical video replay to improve technical and nontechnical performance of surgeons.

SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education).

BACKGROUND: Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose. METHODS: Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted. RESULTS: The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data. CONCLUSION: This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow.

Promoting breast health awareness: Can a sensor-enabled training system for patient education help?

INTRODUCTION: According to a 2009 study published in the Journal of Clinical Oncology, 79% of women (N = 222) diagnosed with breast cancer reported that they identified their cancers through breast self-exam (BSE). However, the U.S. Preventative Services Task Force does not require clinicians to teach women how to perform BSE. METHODS: To address this grave challenge, our team at the Technology Enabled Clinical Improvement (TECI) Center has developed a mobile, sensor-enabled haptic training system to teach women proper BSE technique. To validate the efficacy of the training system, our team deployed a data collection at the 2019 Breast Cancer and African Americans (BCAA) event where survey, sensor, and anecdotal data were collected from 61 participants. The custom-built breast model used in this study had a single, hard mass embedded in it. RESULTS: Participants at the BCAA event were able to successfully identify the mass 65% of the time and used an average force of 7.2 N. When looking at participants' confidence in their abilities to perform BSE, only 10% of respondents answered "very confident" pre-training whereas post-training, the reporting for "very confident" jumped to 66% (p < 0.01). CONCLUSION: By comparison, our previous work revealed that practitioners who use less than 10 N of force are 70% more likely to miss a lesion. The integration of sensors into the BSE haptic training system allowed for objective, evidence-based assessment of hands-on skill. In addition to teaching women proper BSE technique, this training empowered women to be informed advocates in their breast health journey. Future community-based training/feedback sessions will allow for continuous advancement of the training system.

The Quantified Surgeon: A Glimpse Into the Future of Surgical Metrics and Outcomes.

This paper summarizes key points of the 2023 Southeastern Surgical Congress Laws Lecture. The focus of the presentation was on the use of advanced engineering technology to quantify surgical mastery. New concepts relating to the visual-haptic loop, mastery and perception, and mastery and technical decisions were introduced and shown in an empirical fashion to have relevance in procedural outcomes in a simulated setting. The major takeaway point is that surgical mastery can be quantified using advanced engineering technology, and this process will help to shorten the learning curve.

An American Board of Surgery Pilot of Video Assessment of Surgeon Technical Performance in Surgery.

OBJECTIVE: The American Board of Surgery (ABS) sought to investigate the suitability of video-based assessment (VBA) as an adjunct to certification for assessing technical skills. BACKGROUND: Board certification is based on the successful completion of a residency program coupled with knowledge and reasoning assessments. VBA is a new modality for evaluating operative skills that have been shown to correlate with patient outcomes after surgery. METHODS: Diplomates of the ABS were initially assessed for background knowledge and interest in VBA. Surgeons were then solicited to participate in the pilot. Three commercially available VBA platforms were identified and used for the pilot assessment. All participants served as reviewers and reviewees for videos. After the interaction, participants were surveyed regarding their experiences and recommendations to the ABS. RESULTS: To the initial survey, 4853/25,715 diplomates responded. The majority were neither familiar with VBA, nor the tools used for operative assessments. Two hundred seventy-four surgeons actively engaged in the subsequent pilot. One hundred sixty-nine surgeons completed the postpilot survey. Most participants found the process straightforward. Of the participants, 74% felt that the feedback would help their surgical practice. The majority (81%) remain interested in VBA for continuing medical education credits. Using VBA in continuous certification could improve surgeon skills felt by 70%. Two-thirds of participants felt VBA could help identify and remediate underperforming surgeons. Identified barriers to VBA included limitations for open surgery, privacy issues, and technical concerns. CONCLUSIONS: VBA is promising as an adjunct to the current board certification process and should be further considered by the ABS.

Generating Rare Surgical Events Using CycleGAN: Addressing Lack of Data for Artificial Intelligence Event Recognition.

INTRODUCTION: Artificial Intelligence (AI) has shown promise in facilitating surgical video review through automatic recognition of surgical activities/events. There are few public video data sources that demonstrate critical yet rare events which are insufficient to train AI for reliable video event recognition. We suggest that a generative AI algorithm can create artificial massive bleeding images for minimally invasive lobectomy that can be used to augment the current lack of data in this field. MATERIALS AND METHODS: A generative adversarial network (GAN) algorithm was used (CycleGAN) to generate artificial massive bleeding event images. To train CycleGAN, six videos of minimally invasive lobectomies were utilized from which 1819 frames of nonbleeding instances and 3178 frames of massive bleeding instances were used. RESULTS: The performance of the CycleGAN algorithm was tested on a new video that was not used during the training process. The trained CycleGAN was able to alter the laparoscopic lobectomy images according to their corresponding massive bleeding images, where the contents of the original images were preserved (e.g., location of tools in the scene) and the style of each image is changed to massive bleeding (i.e., blood automatically added to appropriate locations on the images). CONCLUSIONS: The result could suggest a promising approach to supplement the lack of data for the rare massive bleeding event that can occur during minimally invasive lobectomy. Future work could be dedicated to developing AI algorithms to identify surgical strategies and actions that potentially lead to massive bleeding and warn surgeons prior to this event occurrence.

AI-Based Video Segmentation: Procedural Steps or Basic Maneuvers?

INTRODUCTION: Video-based review of surgical procedures has proven to be useful in training by enabling efficiency in the qualitative assessment of surgical skill and intraoperative decision-making. Current video segmentation protocols focus largely on procedural steps. Although some operations are more complex than others, many of the steps in any given procedure involve an intricate choreography of basic maneuvers such as suturing, knot tying, and cutting. The use of these maneuvers at certain procedural steps can convey information that aids in the assessment of the complexity of the procedure, surgical preference, and skill. Our study aims to develop and evaluate an algorithm to identify these maneuvers. METHODS: A standard deep learning architecture was used to differentiate between suture throws, knot ties, and suture cutting on a data set comprised of videos from practicing clinicians (N = 52) who participated in a simulated enterotomy repair. Perception of the added value to traditional artificial intelligence segmentation was explored by qualitatively examining the utility of identifying maneuvers in a subset of steps for an open colon resection. RESULTS: An accuracy of 84% was reached in differentiating maneuvers. The precision in detecting the basic maneuvers was 87.9%, 60%, and 90.9% for suture throws, knot ties, and suture cutting, respectively. The qualitative concept mapping confirmed realistic scenarios that could benefit from basic maneuver identification. CONCLUSIONS: Basic maneuvers can indicate error management activity or safety measures and allow for the assessment of skill. Our deep learning algorithm identified basic maneuvers with reasonable accuracy. Such models can aid in artificial intelligence-assisted video review by providing additional information that can complement traditional video segmentation protocols.

Do Individual Surgeon Preferences Affect Procedural Outcomes?

OBJECTIVES: Surgeon preferences such as instrument and suture selection and idiosyncratic approaches to individual procedure steps have been largely viewed as minor differences in the surgical workflow. We hypothesized that idiosyncratic approaches could be quantified and shown to have measurable effects on procedural outcomes. METHODS: At the American College of Surgeons (ACS) Clinical Congress, experienced surgeons volunteered to wear motion tracking sensors and be videotaped while evaluating a loop of porcine intestines to identify and repair 2 preconfigured, standardized enterotomies. Video annotation was used to identify individual surgeon preferences and motion data was used to quantify surgical actions. χ 2 analysis was used to determine whether surgical preferences were associated with procedure outcomes (bowel leak). RESULTS: Surgeons' (N=255) preferences were categorized into 4 technical decisions. Three out of the 4 technical decisions (repaired injuries together, double-layer closure, corner-stitches vs no corner-stitches) played a significant role in outcomes, P <0.05. Running versus interrupted did not affect outcomes. Motion analysis revealed significant differences in average operative times (leak: 6.67 min vs no leak: 8.88 min, P =0.0004) and work effort (leak-path length=36.86 cm vs no leak-path length=49.99 cm, P =0.001). Surgeons who took the riskiest path but did not leak had better bimanual dexterity (leak=0.21/1.0 vs no leak=0.33/1.0, P =0.047) and placed more sutures during the repair (leak=4.69 sutures vs no leak=6.09 sutures, P =0.03). CONCLUSIONS: Our results show that individual preferences affect technical decisions and play a significant role in procedural outcomes. Future analysis in more complex procedures may make major contributions to our understanding of contributors to procedure outcomes.