Effects of explainable artificial intelligence in neurology decision support.

OBJECTIVE: Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. METHODS: We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. RESULTS: We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. INTERPRETATION: xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.

Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks.

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system (𝑝 < 0.01). Finally, we report an interesting relationship between usability, alignment, and perceived intelligence wherein greater perceived alignment of recommendations with one's own preferences leads to higher perceived intelligence (𝑝 < 0.01) and higher usability (𝑝 < 0.01).

Using Digital Biomarkers for Objective Assessment of Perfusionists' Workload and Acute Stress During Cardiac Surgery.

The cardiac operating room (OR) is a high-risk, high-stakes environment inserted into a complex socio-technical healthcare system. During cardiopulmonary bypass (CPB), the most critical phase of cardiac surgery, the perfusionist has a crucial role within the interprofessional OR team, being responsible for optimizing patient perfusion while coordinating other tasks with the surgeon, anesthesiologist, and nurses. The aim of this study was to investigate objective digital biomarkers of perfusionists' workload and stress derived from heart rate variability (HRV) metrics captured via a wearable physiological sensor in a real cardiac OR. We explored the relationships between several HRV parameters and validated self-report measures of surgical task workload (SURG-TLX) and acute stress (STAI-SF), as well as surgical processes and outcome measures. We found that the frequency-domain HRV parameter HF relative power - FFT (%) presented the strongest association with task workload (correlation coefficient: -0.491, p-value: 0.003). We also found that the time-domain HRV parameter RMSSD (ms) presented the strongest correlation with perfusionists' acute stress (correlation coefficient: -0.489, p-value: 0.005). A few workload and stress biomarkers were also associated with bypass time and patient length of stay in the hospital. The findings from this study will inform future research regarding which HRV-based biomarkers are best suited for the development of cognitive support systems capable of monitoring surgical workload and stress in real time.

Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist.

Artificial intelligence (AI) and a popular branch of AI known as machine learning (ML) are increasingly being utilized in medicine and to inform medical research. This review provides an overview of AI and ML (AI/ML), including definitions of common terms. We discuss the history of AI and provide instances of how AI/ML can be applied to pediatric neurology. Examples include imaging in neuro-oncology, autism diagnosis, diagnosis from charts, epilepsy, cerebral palsy, and neonatal neurology. Topics such as supervised learning, unsupervised learning, and reinforcement learning are discussed.

Assessing Team Situational Awareness in the Operating Room via Computer Vision.

Situational awareness (SA) at both individual and team levels, plays a critical role in the operating room (OR). During the pre-incision time-out, the entire OR team comes together to deploy the surgical safety checklist (SSC). Worldwide, the implementation of the SSC has been shown to reduce intraoperative complications and mortality among surgical patients. In this study, we investigated the feasibility of applying computer vision analysis on surgical videos to extract team motion metrics that could differentiate teams with good SA from those with poor SA during the pre-incision time-out. We used a validated observation-based tool to assess SA, and a computer vision software to measure body position and motion patterns in the OR. Our findings showed that it is feasible to extract surgical team motion metrics captured via off-the-shelf OR cameras. Entropy as a measure of the level of team organization was able to distinguish surgical teams with good and poor SA. These findings corroborate existing studies showing that computer vision-based motion metrics have the potential to integrate traditional observation-based performance assessments in the OR.

Improving Surgical Triage in Spine Clinic: Predicting Likelihood of Surgery Using Machine Learning.

BACKGROUND: Correctly triaging patients to a surgeon or a nonoperative provider is an important part of the referral process. Clinics typically triage new patients based on simple intake questions. This is time-consuming and does not incorporate objective data. Our goal was to use machine learning to more accurately screen surgical candidates seen in a spine clinic. METHODS: Using questionnaire data and magnetic resonance imaging reports, a set of artificial neural networks was trained to predict whether a patient would be recommended for spine surgery. Questionnaire responses included demographics, chief complaint, and pain characteristics. The primary end point was the surgeon's determination of whether a patient was an operative candidate. Model accuracy in predicting this end point was assessed using a separate subset of patients. RESULTS: The retrospective dataset included 1663 patients in cervical and lumbar cohorts. Questionnaire data were available for all participants, and magnetic resonance imaging reports were available for 242 patients. Within 6 months of initial evaluation, 717 (43.1%) patients were deemed surgical candidates by the surgeon. Our models predicted surgeons' recommendations with area under the curve scores of 0.686 for lumbar (positive predictive value 66%, negative predictive value 80%) and 0.821 for cervical (positive predictive value 83%, negative predictive value 85%) patients. CONCLUSIONS: Our models used patient data to accurately predict whether patients will receive a surgical recommendation. The high negative predictive value demonstrates that this approach can reduce the burden of nonsurgical patients in surgery clinic without losing many surgical candidates. This could reduce unnecessary visits for patients, increase the proportion of operative candidates seen by surgeons, and improve quality of patient care.

Queueing theoretic analysis of labor and delivery : Understanding management styles and C-section rates.

Childbirth is a complex clinical service requiring the coordinated support of highly trained healthcare professionals as well as management of a finite set of critical resources (such as staff and beds) to provide safe care. The mode of delivery (vaginal delivery or cesarean section) has a significant effect on labor and delivery resource needs. Further, resource management decisions may impact the amount of time a physician or nurse is able to spend with any given patient. In this work, we employ queueing theory to model one year of transactional patient information at a tertiary care center in Boston, Massachusetts. First, we observe that the M/G/∞ model effectively predicts patient flow in an obstetrics department. This model captures the dynamics of labor and delivery where patients arrive randomly during the day, the duration of their stay is based on their individual acuity, and their labor progresses at some rate irrespective of whether they are given a bed. Second, using our queueing theoretic model, we show that reducing the rate of cesarean section - a current quality improvement goal in American obstetrics - may have important consequences with regard to the resource needs of a hospital. We also estimate the potential financial impact of these resource needs from the hospital perspective. Third, we report that application of our model to an analysis of potential patient coverage strategies supports the adoption of team-based care, in which attending physicians share responsibilities for patients.

Association Between Labor and Delivery Unit Census and Delays in Patient Management: Findings From a Computer Simulation Module.

OBJECTIVE: To demonstrate the association between increases in labor and delivery unit census and delays in patient care decisions using a computer simulation module. METHODS: This was an observational cohort study of labor and delivery unit nurse managers. We developed a computer module that simulates the physical layout and clinical activity of the labor and delivery unit at our tertiary care academic medical center, in which players act as clinical managers in dynamically allocating nursing staff and beds as patients arrive, progress in labor, and undergo procedures. We exposed nurse managers to variation in patient census and measured the delays in resource decisions over the course of a simulated shift. We used mixed logistic and linear regression models to analyze the associations between patient census and delays in patient care. RESULTS: Thirteen nurse managers participated in the study and completed 17 12-hour shifts, or 204 simulated hours of decision-making. All participants reported the simulation module reflected their real-life experiences at least somewhat well. We observed 1.47-increased odds (95% CI 1.18-1.82) of recommending a patient ambulate in early labor for every additional patient on the labor and delivery unit. For every additional patient on the labor and delivery unit, there was a 15.9-minute delay between delivery and transfer to the postpartum unit (95% CI 2.4-29.3). For every additional patient in the waiting room, we observed a 33.3-minute delay in the time patients spent in the waiting room (95% CI 23.2-43.5) and a 14.3-minute delay in moving a patient in need of a cesarean delivery to the operating room (95% CI 2.8-25.8). CONCLUSION: Increasing labor and delivery unit census is associated with patient care delays in a computer simulation. Computer simulation is a feasible and valid method of demonstrating the sensitivity of care decisions to shifts in patient volume.