Implications of Large Language Models for Quality and Efficiency of Neurologic Care: Emerging Issues in Neurology.

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

Neurologic Alternative Payment Models-Bridging the Gap.

This Viewpoint discusses alternative payments models and the responsibilities of health care professionals.

Improving Neurology Clinical Care With Natural Language Processing Tools.

The integration of natural language processing (NLP) tools into neurology workflows has the potential to significantly enhance clinical care. However, it is important to address the limitations and risks associated with integrating this new technology. Recent advances in transformer-based NLP algorithms (e.g., GPT, BERT) could augment neurology clinical care by summarizing patient health information, suggesting care options, and assisting research involving large datasets. However, these NLP platforms have potential risks including fabricated facts and data security and substantial barriers for implementation. Although these risks and barriers need to be considered, the benefits for providers, patients, and communities are substantial. With these systems achieving greater functionality and the pace of medical need increasing, integrating these tools into clinical care may prove not only beneficial but necessary. Further investigation is needed to design implementation strategies, mitigate risks, and overcome barriers.

Antiamyloid Monoclonal Antibody Therapy for Alzheimer Disease: Emerging Issues in Neurology.

With recent data demonstrating that lecanemab treatment can slow cognitive and functional decline in early symptomatic Alzheimer disease (AD), it is widely anticipated that this drug and potentially other monoclonal antibody infusions targeting ß-amyloid protein will imminently be realistic options for some patients with AD. Given that these new antiamyloid monoclonal antibodies (mAbs) are associated with nontrivial risks and burdens of treatment that are radically different from current mainstays of AD management, effectively and equitably translating their use to real-world clinical care will require systematic and practice-specific modifications to existing workflows and infrastructure. In this Emerging Issues in Neurology article, we provide practical guidance for a wide audience of neurology clinicians on logistic adaptations and decision making around emerging antiamyloid mAbs. Specifically, we briefly summarize the rationale and available evidence supporting antiamyloid mAb use in AD to facilitate appropriate communication with patients and care partners on potential benefits. We also discuss pragmatic approaches to optimizing patient selection and treatment monitoring, with a particular focus on the value of incorporating shared decision making and multidisciplinary collaboration. In addition, we review some of the recognized limitations of current knowledge and highlight areas of future evolution to guide the development of sustainable and flexible models for treatment and follow-up. As the field enters a new era with disease-modifying treatment options for AD, it will be critical for neurology practices to prepare and continually innovate to ensure optimal outcomes for patients.

Measuring Ambulatory Racial and Ethnic Neurologic Disparities With the Axon Registry.

Background and Objectives: The primary objective is to examine potential racial and ethnic (R/E) disparities in ambulatory neurology quality measures within the American Academy of Neurology Axon Registry. R/E disparities in neurologic US morbidity and mortality have been clearly documented. Despite these findings, there have been no nationwide examinations of how ambulatory neurologic care affects these negative health outcomes. Methods: This was a retrospective nonrandomized cohort study of patients in the AAN Axon Registry. The Axon Registry is a neurology-specific outpatient quality registry that collects, reports, and analyzes real-world deidentified electronic health record (EHR) data. Patients were included in the study if they contributed toward one of the selected quality measures for multiple sclerosis, epilepsy, Parkinson disease, or headache during the study period of January 1, 2019-December 31, 2019. Descriptive analyses of patient demographics were performed and then stratified by race and ethnicity. Results: There were a total of 633,672 patients included in these analyses. Separate analyses were performed for race (64% White, 8% Black, 1% Asian, and 27% unknown) and ethnicity (52% not Hispanic, 5% Hispanic, and 43% unknown). The mean age ranged from 18 to 55 years, with 61% female and 39% male. Quality measures were chosen based on completeness of R/E data and were either process or outcomes focused. Statistically significant differences were noted after controlling for multiple comparisons. Discussion: The large proportion of missing or unknown R/E data and low overall rate of performance on these quality measures made the relevance of small differences difficult to determine. This analysis demonstrates the feasibility of using the Axon Registry to assess neurologic disparities in outpatient care. More education and training are required on the accurate capture of R/E data in the EHR.

Neurologic Care of Forcibly Displaced Persons: Emerging Issues in Neurology.

There is a growing number of forcibly displaced persons (FDPs) worldwide. With more than 100 million people forcibly displaced today, there is an urgent mandate to understand the neurologic care needs of this population and how neurologists and other health care workers can most effectively provide that care. In this Emerging Issues in Neurology article, we attempt to (1) define the scope of the problem of providing neurologic care to FDPs, (2) highlight commonly encountered clinical challenges related to neurologic care of FDPs, and (3) provide useful clinical information for neurologists and other clinicians who deliver care to FDPs with neurologic needs. We address the terminology of forcible displacement and how terms may differ across a person's migration journey. Common challenges encountered by FDPs with neurologic needs across settings include loss of support systems, loss of personal health information, language barriers and differing expression of symptoms, differing belief systems, epidemiologic patterns of disease unfamiliar to the clinician, and patients' fear and perceived risks of engaging with health systems. Practical approaches are shared for clinicians who encounter an FDP with a neurologic presentation. Finally, the article discusses many unmet neurologic needs of FDPs, which require significant investment. These include addressing lapses in neurologic care during displacement and understanding the effects of forcible displacement on people with chronic neurologic conditions. Future research and educational resources should focus on improving epidemiologic intelligence for neurologic conditions across geographies, developing curricula for optimizing the neurologic care of FDPs, and evaluating the most appropriate and effective uses of health technologies in humanitarian settings.

US Health Care System in 2035: The Neurology Future Forecasting Series.

Evolution of the US health care system has been punctuated by periods of rapid change. In the coming decades there will be meaningful and potentially disruptive developments in health care delivery mechanisms, the policy environment, and the populations for whom we provide care. Neurologists will need to adapt to changing patient expectations, market dynamics, and regulatory structures to thrive in the future health care environment. This article describes a forecast of potential changes in the US health care system by 2035, an assessment of the implications for the field of neurology, and a rationale for long-term strategic planning to prepare.

Creating a New Set of Milestones for the Clinical Neurophysiology Fellowship.

INTRODUCTION: The Accreditation Council for Graduate Medical Education and the American Board of Psychiatry and Neurology first developed milestones for the clinical neurophysiology (CNP) fellowship in 2015. The milestones provide a comprehensive evaluation of the fellow's development based on six domains of competency. Here, we describe the development of a new set of milestones for CNP fellowship with level 1 as the incoming level, level 4 as the goal for graduation, and level 5 as the aspirational level that may not be achieved. METHODS: Committee members were nominated or volunteered to participate in the milestones update. Milestone development began with the creation of a shared mental model of the ideal skills and knowledge a graduating CNP fellow should attain. RESULTS: The CNP committee met virtually 7 times for a total of 14 meeting hours. Nine Patient Care and five Medical Knowledge milestones evolved from the seven Patient Care and six Medical Knowledge milestones that were in the first iteration. The committee incorporated 11 "Harmonized Milestones" into the revision and a supplemental guide was created. CONCLUSIONS: The revised Accreditation Council for Graduate Medical Education milestones for CNP fellowship contain important updates that program directors should review against their curricula to identify any gaps in learning. Program leadership should take note of two new Patient Care milestones for telemedicine and intraoperative monitoring. Clinical neurophysiology fellowships are not designed to provide level 4 competency across all milestones. The revised milestones should be viewed within the context of an individual program's goals.