It's not the arrow, it's the archer: the role of the surgeon leader in a safety driven-era.

BACKGROUND: In an era where team communication and patient safety are paramount, standardized tools have been deemed critical to safe, efficient practice. In some cases-perhaps most notably in the surgical safety checklist (SSC)-these tools have been elevated as the key to safe patient care. However, effects of the SSC on patient safety in practice remain mixed. We explore the role and impact of the surgeon leader in the use of structured communication tools to understand how surgeon engagement impacts intraoperative teamwork. METHODS: Using a constructivist grounded theory approach, OR staff members (surgeons, anesthetists, nurses and perfusionists) were recruited to participate in a one-on-one semi-structured interview. The interview explored participant experiences working in the OR, focusing on the role and impact of the surgeon as leader. RESULTS: Engaged use of the surgical safety checklist by the attending surgeon had the potential to improve teamwork in the operating room. Surgeons who used the checklist to engage with their team and facilitate group discussion were able to avoid tensions later in the operation typically arising from lack of situation awareness and familiarity with team member experience levels. Surgeons who engaged with the SSC as more than a memory aid were able to foster a better team environment. CONCLUSIONS: Surgeons can harness their role as leader in the operating room by engaging with structured communication tools such as the SSC to foster improved teamwork.

Machine Learning-Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation.

BACKGROUND: National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects. OBJECTIVE: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth-the percentage change in total cumulative cases-across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms. METHODS: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non-time series machine learning models in predicting confirmed infection growth. We used three validation methods-in-distribution, out-of-distribution, and country-based cross-validation-for the evaluation, each of which was applicable to a different use case of the models. RESULTS: Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case. CONCLUSIONS: This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections.

Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study.

BACKGROUND: Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of releasing these models. OBJECTIVE: This paper aims to demonstrate that traditional word embeddings created on clinical corpora that have been deidentified by removing personal health information (PHI) can nonetheless be exploited to reveal sensitive patient information. METHODS: We used embeddings created from 400,000 doctor-written consultation notes and experimented with 3 common word embedding methods to explore the privacy-preserving properties of each. RESULTS: We found that if publicly released embeddings are trained from a corpus anonymized by PHI removal, it is possible to reconstruct up to 68.5% (n=411/600) of the full names that remain in the deidentified corpus and associated sensitive information to specific patients in the corpus from which the embeddings were created. We also found that the distance between the word vector representation of a patient's name and a diagnostic billing code is informative and differs significantly from the distance between the name and a code not billed for that patient. CONCLUSIONS: Special care must be taken when sharing word embeddings created from clinical texts, as current approaches may compromise patient privacy. If PHI removal is used for anonymization before traditional word embeddings are trained, it is possible to attribute sensitive information to patients who have not been fully deidentified by the (necessarily imperfect) removal algorithms. A promising alternative (ie, anonymization by PHI replacement) may avoid these flaws. Our results are timely and critical, as an increasing number of researchers are pushing for publicly available health data.

A survey of word embeddings for clinical text.

Representing words as numerical vectors based on the contexts in which they appear has become the de facto method of analyzing text with machine learning. In this paper, we provide a guide for training these representations on clinical text data, using a survey of relevant research. Specifically, we discuss different types of word representations, clinical text corpora, available pre-trained clinical word vector embeddings, intrinsic and extrinsic evaluation, applications, and limitations of these approaches. This work can be used as a blueprint for clinicians and healthcare workers who may want to incorporate clinical text features in their own models and applications.

Artificial Intelligence and the Implementation Challenge.

BACKGROUND: Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. OBJECTIVE: In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). METHODS: After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. RESULTS: Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. CONCLUSIONS: If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

Automatically determining cause of death from verbal autopsy narratives.

BACKGROUND: A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone. METHODS: After preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: naïve Bayes, random forest, support vector machines, and a neural network. RESULTS: For individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods. CONCLUSIONS: Our narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.

Treatment intensity and childhood apraxia of speech.

BACKGROUND: Intensive treatment has been repeatedly recommended for the treatment of speech deficits in childhood apraxia of speech (CAS). However, differences in treatment outcomes as a function of treatment intensity have not been systematically studied in this population. AIM: To investigate the effects of treatment intensity on outcome measures related to articulation, functional communication and speech intelligibility for children with CAS undergoing individual motor speech intervention. METHODS & PROCEDURES: A total of 37 children (32-54 months of age) with CAS received 1×/week (lower intensity) or 2×/week (higher intensity) individual motor speech treatment for 10 weeks. Assessments were carried out before and after a 10-week treatment block to study the effects of variations in treatment intensity on the outcome measures. OUTCOMES & RESULTS: The results indicated that only higher intensity treatment (2×/week) led to significantly better outcomes for articulation and functional communication compared with 1×/week (lower intensity) intervention. Further, neither lower nor higher intensity treatment yielded a significant change for speech intelligibility at the word or sentence level. In general, effect sizes for the higher intensity treatment groups were larger for most variables compared with the lower intensity treatment group. CONCLUSIONS & IMPLICATIONS: Overall, the results of the current study may allow for modification of service delivery and facilitate the development of an evidence-based care pathway for children with CAS.

Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.

PURPOSE: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data. METHODS: Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequently balanced our dataset and used the Boruta algorithm to determine feature selection. Massive transfusion was defined as five units at 4 h and ten units at 24 h. Six machine learning models were trained on the balanced dataset and tested on the original. RESULTS: A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the receiver-operating characteristic curve of 0.83. Extreme gradient boost modeling slightly outperformed and demonstrated adequate predictive performance with pre-hospital data only, as well as 4-h transfusion thresholds. CONCLUSIONS: We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential utility of artificial intelligence as a clinical decision support tool.

Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report.

BACKGROUND: A critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD. METHODS: We followed 97 patients with remitted MDD for an average of 394 days. Patients completed weekly self-ratings of depression and anxiety symptoms using the Quick Inventory of Depressive Symptoms (QIDS-SR) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Using these longitudinal ratings we computed DACS as random slopes in a linear mixed effects model reflecting individual-specific degree of correlation between depression and anxiety across time points. We then tested DACS as an independent variable in a Cox proportional hazards model to predict relapse. RESULTS: A total of 28 patients (29 %) relapsed during the follow-up period. DACS significantly predicted confirmed relapse (hazard ratio [HR] 1.5, 95 % CI [1.01, 2.22], p = 0.043; Concordance 0.79 [SE 0.04]). This effect was independent of baseline depressive or anxiety symptoms or their average levels over the follow-up period, and was identifiable more than one month before relapse onset. LIMITATIONS: Small sample size, in a single study. Narrow phenotype and comorbidity profiles. CONCLUSIONS: DACS may offer opportunities for developing novel strategies for personalized monitoring, early detection, and intervention. Future studies should replicate our findings in larger, diverse patient populations, develop individual patient prediction models, and explore the underlying mechanisms that govern the relationship of DACS and relapse.

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP.

OBJECTIVE: Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC. MATERIALS AND METHODS: A retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy. RESULTS: Totally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4-day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS-NSQIP calculator's performance (0.23, 59%). CONCLUSION: We developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS-NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice. LEVEL OF EVIDENCE: Level 3 Laryngoscope, 2024.

Conformal mirror descent with logarithmic divergences.

The logarithmic divergence is an extension of the Bregman divergence motivated by optimal transport and a generalized convex duality, and satisfies many remarkable properties. Using the geometry induced by the logarithmic divergence, we introduce a generalization of continuous time mirror descent that we term the conformal mirror descent. We derive its dynamics under a generalized mirror map, and show that it is a time change of a corresponding Hessian gradient flow. We also prove convergence results in continuous time. We apply the conformal mirror descent to online estimation of a generalized exponential family, and construct a family of gradient flows on the unit simplex via the Dirichlet optimal transport problem.

Contextualizing the tone of the operating room in practice: drawing on the literature to connect the dots.

The study of teamwork in the operating room has made significant strides in uncovering key constructs which shape safe and effective intraoperative care. However, in recent years, there have been calls to understand teamwork in the operating room more fully by embracing the complexity of the intraoperative environment. We propose the construct of tone as a useful lens through which to understand intraoperative teamwork. In this article, we review the literature on culture, shared mental models, and psychological safety, linking each to the construct of tone. By identifying tone as a theoretical orientation to demonstrate the overlap between these concepts, we aim to provide a starting point for new ways to understand intraoperative team dynamics.

Exploring the Use of Natural Language Processing for Objective Assessment of Disorganized Speech in Schizophrenia.

Objective: Measurement-based care tools in psychiatry are useful for symptom monitoring and detecting response to treatment, but methods for quick and objective measurement are lacking especially for acute psychosis. The aim of this study was to explore potential language markers, detected by natural language processing (NLP) methods, as a means to objectively measure the severity of psychotic symptoms of schizophrenia in an acute clinical setting. Methods: Twenty-two speech samples were collected from seven participants who were hospitalized for schizophrenia, and their symptoms were evaluated over time with SAPS/SANS and TLC scales. Linguistic features were extracted from the speech data using machine learning techniques. Spearman's correlation was performed to examine the relationship between linguistic features and symptoms. Various machine learning models were evaluated by cross-validation methods for their ability to predict symptom severity using the linguistic markers. Results: Reduced lexical richness and syntactic complexity were characteristic of negative symptoms, while lower content density and more repetitions in speech were predictors of positive symptoms. Machine learning models predicted severity of alogia, illogicality, poverty of speech, social inattentiveness, and TLC scores with up to 82% accuracy. Additionally, speech incoherence was quantifiable through language markers derived from NLP methods. Conclusions: These preliminary findings suggest that NLP may be useful in identifying clinically relevant language markers of schizophrenia, which can enhance objectivity in symptom monitoring during hospitalization. Further work is needed to replicate these findings in a larger data set and explore methods for feasible implementation in practice.

Deep Learning Model for Automated Trainee Assessment During High-Fidelity Simulation.

PROBLEM: Implementation of competency-based medical education has necessitated more frequent trainee assessments. Use of simulation as an assessment tool is limited by access to trained examiners, cost, and concerns with interrater reliability. Developing an automated tool for pass/fail assessment of trainees in simulation could improve accessibility and quality assurance of assessments. This study aimed to develop an automated assessment model using deep learning techniques to assess performance of anesthesiology trainees in a simulated critical event. APPROACH: The authors retrospectively analyzed anaphylaxis simulation videos to train and validate a deep learning model. They used an anaphylactic shock simulation video database from an established simulation curriculum, integrating a convenience sample of 52 usable videos. The core part of the model, developed between July 2019 and July 2020, is a bidirectional transformer encoder. OUTCOMES: The main outcome was the F1 score, accuracy, recall, and precision of the automated assessment model in analyzing pass/fail of trainees in simulation videos. Five models were developed and evaluated. The strongest model was model 1 with an accuracy of 71% and an F1 score of 0.68. NEXT STEPS: The authors demonstrated the feasibility of developing a deep learning model from a simulation database that can be used for automated assessment of medical trainees in a simulated anaphylaxis scenario. The important next steps are to (1) integrate a larger simulation dataset to improve the accuracy of the model; (2) assess the accuracy of the model on alternative anaphylaxis simulations, additional medical disciplines, and alternative medical education evaluation modalities; and (3) gather feedback from education leadership and clinician educators surrounding the perceived strengths and weaknesses of deep learning models for simulation assessment. Overall, this novel approach for performance prediction has broad implications in medical education and assessment.

Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care.

The increasingly sophisticated and rapidly evolving application of artificial intelligence in medicine is transforming how health care is delivered, highlighting a need for current and future physicians to develop basic competency in the data science that underlies this topic. Medical educators must consider how to incorporate central concepts in data science into their core curricula to train physicians of the future. Similar to how the advent of diagnostic imaging required the physician to understand, interpret, and explain the relevant results to patients, physicians of the future should be able to explain to patients the benefits and limitations of management plans guided by artificial intelligence. We outline major content domains and associated learning outcomes in data science applicable to medical student curricula, suggest ways to incorporate these themes into existing curricula, and note potential implementation barriers and solutions to optimize the integration of this content.

Improving estimates of pertussis burden in Ontario, Canada 2010-2017 by combining validation and capture-recapture methodologies.

An underestimation of pertussis burden has impeded understanding of transmission and disallows effective policy and prevention to be prioritized and enacted. Capture-recapture analyses can improve burden estimates; however, uncertainty remains around incorporating health administrative data due to accuracy limitations. The aim of this study is to explore the impact of pertussis case definitions and data accuracy on capture-recapture estimates. We used a dataset from March 7, 2010 to December 31, 2017 comprised of pertussis case report, laboratory, and health administrative data. We compared Chao capture-recapture abundance estimates using prevalence, incidence, and adjusted false positive case definitions. The latter was developed by removing the proportion of false positive physician billing code-only case episodes after validation. We calculated sensitivity by dividing the number of observed cases by abundance. Abundance estimates demonstrated that a high proportion of cases were missed by all sources. Under the primary analysis, the highest sensitivity of 78.5% (95% CI 76.2-80.9%) for those less than one year of age was obtained using all sources after adjusting for false positives, which dropped to 43.1% (95% CI 42.4-43.8%) for those one year of age or older. Most code-only episodes were false positives (91.0%), leading to considerably lower abundance estimates and improvements in laboratory testing and case report sensitivity using this definition. Accuracy limitations can be accounted for in capture-recapture analyses using different case definitions and adjustment. The latter enhanced the validity of estimates, furthering the utility of capture-recapture methods to epidemiological research. Findings demonstrated that all sources consistently fail to detect pertussis cases. This is differential by age, suggesting ascertainment and testing bias. Results demonstrate the value of incorporating real time health administrative data into public health surveillance if accuracy limitations can be addressed.

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence.

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.

Developing a reference standard for pertussis by applying a stratified sampling strategy to electronic medical record data.

PURPOSE: Pertussis surveillance remains essential in Canada, but ascertainment bias limits the accuracy of surveillance data. Introducing other sources to improve detection has highlighted the importance of validation. However, challenges arise due to low prevalence, and oversampling suspected cases can introduce partial verification bias. The aim of this study was to build a reference standard for pertussis validation studies that provides adequate analytic precision and minimizes bias. METHODS: We used a stratified strategy to sample the reference standard from a primary care electronic medical record cohort. We incorporated abstractor notes into definite, possible, ruled-out, and no mention of pertussis classifications which were based on surveillance case definitions. RESULTS: We abstracted eight hundred records from the cohort of 404,922. There were 208 (26%) definite and 261 (32.6%) possible prevalent pertussis cases. Classifications demonstrated a wide variety of case severities. Abstraction reliability was moderate to substantial based on Cohen's kappa and raw percent agreement. CONCLUSIONS: When conducting validation studies for pertussis and other low prevalence diseases, this stratified sampling strategy can be used to develop a reference standard using limited resources. This approach mitigates verification and spectrum bias while providing sufficient precision and incorporating a range of case severities.