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.

A multidisciplinary care model to increase access to neurologic ambulatory care.

BACKGROUND: Expert patient care has been associated with improved outcomes for neurology patients, yet timely access to specialists is challenging. The employment of nurse practitioners (NPs) holds great potential to increase access to neurologic ambulatory care, however little practical guidance exists to date for how this may be achieved. LOCAL PROBLEM: To improve timely care provision for patients with neurologic disease, we employed a multidisciplinary care utilization framework that used NPs to expand clinic appointment availability. METHODS AND INTERVENTION: After evaluating our baseline performance, we applied a standardized approach to the deployment of NPs in neurology clinic with regard to scheduling clinic sessions and patient appointments. The primary outcome measure was appointment availability measured over 6 months preintervention (June to November 2016) and 6 months postintervention (June to November 2017). Secondary measures included NP effort allocation. RESULTS: The postintervention period demonstrated an increase in available appointments (3,731 preintervention vs. 4,318 postintervention) and scheduled appointments (2,014 vs. 2,685). Nurse practitioners spent more time practicing at the fullest extent of their licensure. All improvements were accomplished without the hiring of additional staff. CONCLUSIONS: A multidisciplinary care utilization framework for NP employment across neurology subspecialties resulted in an increase in appointment availability. Furthermore, this model is likely to be sustainable due to provider satisfaction and financial viability.

High performance teamwork training and systems redesign in outpatient oncology.

BACKGROUND: Oncology care is delivered largely in ambulatory settings by interdisciplinary teams. Treatments are often complex, extended in time, dispersed geographically and vulnerable to teamwork failures. To address this risk, we developed and piloted a team training initiative in the breast cancer programme at a comprehensive cancer centre. METHODS: Based on clinic observations, interviews with key staff and analyses of incident reports, we developed interventions to address four high-risk areas: (1) miscommunication of chemotherapy order changes on the day of treatment; (2) missing orders on treatment days without concurrent physician appointments; (3) poor follow-up with team members about active patient issues; and (4) conflict between providers and staff. The project team developed protocols and agreements to address team members' roles, responsibilities and behaviours. RESULTS: Using a train-the-trainer model, 92% of breast cancer staff completed training. The incidence of missing orders for unlinked visits decreased from 30% to 2% (p<0.001). Patient satisfaction scores regarding coordination of care improved from 93 to 97 (p=0.026). Providers, infusion nurses and support staff reported improvement in efficiency (75%, 86%, 90%), quality (82%, 93%, 93%) and safety (92%, 92%, 90%) of care, and more respectful behaviour (92%, 79%, 83%) and improved relationships among team members (91%, 85%, 92%). Although most clinicians reported a decrease in non-communicated changes, there was insufficient statistical power to detect a difference. CONCLUSIONS: Team training improved communication, task coordination and perceptions of efficiency, quality, safety and interactions among team members as well as patient perception of care coordination.