Behavioral Health Decision Support Systems and User Interface Design in the Emergency Department.

OBJECTIVE: The objective of this qualitative study is to gauge physician sentiment about an emergency department (ED) clinical decision support (CDS) system implemented in multiple adult EDs within a university hospital system. This CDS system focuses on predicting patients' likelihood of ED recidivism and/or adverse opioid-related events. METHODS: The study was conducted among adult emergency physicians working in three EDs of a single academic health system in Rhode Island. Qualitative, semistructured interviews were conducted with ED physicians. Interviews assessed physicians' prior experience with predictive analytics, thoughts on the alert's placement, design, and content, the alert's overall impact, and potential areas for improvement. Responses were aggregated and common themes identified. RESULTS: Twenty-three interviews were conducted (11 preimplementation and 12 postimplementation). Themes were identified regarding each physician familiarity with predictive analytics, alert rollout, alert appearance and content, and on alert sentiments. Most physicians viewed these alerts as a neutral or positive EHR addition, with responses ranging from neutral to positive. The alert placement was noted to be largely intuitive and nonintrusive. The design of the alert was generally viewed positively. The alert's content was believed to be accurate, although the decision to respond to the alert's call-to-action was physician dependent. Those who tended to ignore the alert did so for a few reasons, including already knowing the information the alert contains, the alert offering information that is not relevant to this particular patient, and the alert not containing enough information to be useful. CONCLUSION: Ultimately, this alert appears to have a marginally positive effect on ED physician workflow. At its most beneficial, the alert reminded physicians to deeply consider the care provided to high-risk populations and to potentially adjust their care and referrals. At its least beneficial, the alert did not affect physician decision-making but was not intrusive to the point of negatively impacting workflow.

Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI.

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 70-75% (95% CI 0.48-0.89), and specificity of 71-79% (95% CI 0.52-0.90) on manual optimization, and an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 65-75% (95% CI 0.43-0.89) and specificity of 75-79% (95% CI 0.56-0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

Machine intelligence in non-invasive endocrine cancer diagnostics.

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.

Advancing health equity with artificial intelligence.

Population and public health are in the midst of an artificial intelligence revolution capable of radically altering existing models of care delivery and practice. Just as AI seeks to mirror human cognition through its data-driven analytics, it can also reflect the biases present in our collective conscience. In this Viewpoint, we use past and counterfactual examples to illustrate the sequelae of unmitigated bias in healthcare artificial intelligence. Past examples indicate that if the benefits of emerging AI technologies are to be realized, consensus around the regulation of algorithmic bias at the policy level is needed to ensure their ethical integration into the health system. This paper puts forth regulatory strategies for uprooting bias in healthcare AI that can inform ongoing efforts to establish a framework for federal oversight. We highlight three overarching oversight principles in bias mitigation that maps to each phase of the algorithm life cycle.

Hospital Surge Preparedness and Response Index.

The Hospital Surge Preparedness and Response Index is an all-hazards template developed by a group of emergency management and disaster medicine experts from the United States. The objective of the Hospital Surge Preparedness and Response Index is to improve planning by linking action items to institutional triggers across the surge capacity continuum. This responder tool is a non-exhaustive, high-level template: administrators should tailor these elements to their individual institutional protocols and constraints for optimal efficiency. The Hospital Surge Preparedness and Response Index can be used to provide administrators with a snapshot of their facility's current service capacity in order to promote efficiency and situational awareness both internally and among regional partners.

The Involvement of Pial Microvessels in Leukocyte Invasion after Mild Traumatic Brain Injury.

The pathophysiological mechanisms underlying mild traumatic brain injury (mTBI) are not well understood, but likely involve neuroinflammation. Here the controlled cortical impact model of mTBI in rats was used to test this hypothesis. Mild TBI caused a rapid (within 6 h post-mTBI) upregulation of synthesis of TNF-α and IL-1ß in the cerebral cortex and hippocampus, followed by an increase in production of neutrophil (CXCL1-3) and monocyte (CCL2) chemoattractants. While astrocytes were not a significant source of CXC chemokines, they highly expressed CCL2. An increase in production of CXC chemokines coincided with the influx of neutrophils into the injured brain. At 6 h post-mTBI, we observed a robust influx of CCL2-expressing neutrophils across pial microvessels into the subarachnoid space (SAS) near the injury site. Mild TBI was not accompanied by any significant influx of neutrophils into the brain parenchyma until 24 h after injury. This was associated with an early induction of expression of intercellular adhesion molecule 1 on the endothelium of the ipsilateral pial, but not intraparenchymal, microvessels. At 6 h post-mTBI, we also observed a robust influx of neutrophils into the ipsilateral cistern of velum interpositum (CVI), a slit-shaped cerebrospinal fluid space located above the 3rd ventricle with highly vascularized pia mater. From SAS and CVI, neutrophils appeared to move along the perivascular spaces to enter the brain parenchyma. The monocyte influx was not observed until 24 h post-mTBI, and these inflammatory cells predominantly entered the ipsilateral SAS and CVI, with a limited invasion of brain parenchyma. These observations indicate that the endothelium of pial microvessels responds to injury differently than that of intraparenchymal microvessels, which may be associated with the lack of astrocytic ensheathment of cerebrovascular endothelium in pial microvessels. These findings also suggest that neuroinflammation represents the potential therapeutic target in mTBI.