Screening for Quality of Life in a Neurology Tic Clinic Using Quality Improvement Methodology.

BACKGROUND: Tic disorders in children often co-occur with other disorders that can significantly impact functioning. Screening for quality of life (QoL) can help identify optimal treatment paths. This quality improvement (QI) study describes implementation of a QoL measure in a busy neurology clinic to help guide psychological intervention for patients with tics. METHODS: Using QI methodology outlined by the Institute for Healthcare Improvement, this study implemented the PedsQL Generic Core (4.0) in an outpatient medical clinic specializing in the diagnosis and treatment of tic disorders. Assembling a research team to design process maps and key driver diagrams helped identify gaps in the screening process. Conducting several plan-do-study-act cycles refined identification of patients appropriate to receive the measure. Over the three-year study, electronic health record notification tools and data collection were increasingly utilized to capture patients' information during their visit. RESULTS: Over 350 unique patients were screened during the assessment period. Electronic means replaced paper measures as time progressed. The percentage of patients completing the measure increased from 0% to 51.9% after the initial implementation of process improvement, advancing to 91.6% after the introduction of electronic measures. This average completion rate was sustained for 15 months. CONCLUSIONS: Using QI methodology helped identify the pragmatics of implementing a QoL assessment to enhance screening practices in a busy medical clinic. Assessment review at the time of appointment helped inform treatment and referral decisions.

Implementing American Academy of Neurology Quality Measures in Antigua Using Quality Improvement Methodology.

Background and Objectives: The American Academy of Neurology has developed quality measures related to various neurologic disorders. A gap exists in the implementation of these measures in the different health care systems. To date, there has been no electronic health care record nor implementation of quality measures in Antigua. Therefore, we aimed to increase the percent of patients who have epilepsy quality measures documented using standardized common data elements in the outpatient neurology clinic at Sir Lester Bird Medical Center from 0% to 80% per week by June 1, 2022 and sustain for 6 months. Methods: We used the Institute for Health care Improvement Model for Improvement methodology. A data use agreement was implemented. Data were displayed using statistical process control charts and the American Society for Quality criteria to determine statistical significance and centerline shifts. Results: Current and future state process maps were developed to determine areas of opportunity for interventions. Interventions were developed following a "Plan-Do-Study-Act cycle." One intervention was the creation of a RedCap survey and database to be used by health care providers during clinical patient encounters. Because of multiple interventions, we achieved a 100% utilization of the survey for clinical care. Discussion: Quality improvement (QI) methodology can be used for implementation of quality measures in various settings to improve patient care outcomes without use of significant resources. Implementation of quality measures can increase efficiency in clinical delivery. Similar QI methodology could be implemented in other resource-limited countries of the Caribbean and globally.

Increasing Provider Utilization of a Seizure Action Plan in the Outpatient Setting.

OBJECTIVES: A seizure action plan (SAP) is a powerful tool that provides actionable information for caregivers during seizures. Guidelines have expressed the need for individualized SAPs. Our quality improvement team aimed to increase implementation of an SAP within a pediatric tertiary center, initially among epilepsy providers and expanded to all neurology providers. METHODS: Process changes were implemented using Plan-Do-Study-Act cycles and data were evaluated monthly using control charts. The team focused on tracking patients who received SAPs and identified opportunities for improvement, including reminders within the electronic medical record, and standardizing clinic processes. A secondary analysis was performed to trend emergency department (ED) use among our patient population. RESULTS: The SAP utilization rate among epilepsy providers increased from a baseline of 39% to 78% by December 2019 and reached the goal of 85% by June 2020, with a further increase to 92% by February 2022 and maintained. The SAP utilization rate among general neurology providers increased from 43% in 2018 to 85% by July 2020, and further increased to 93% by February 2022 and maintained. ED visits of established patients with epilepsy decreased from a baseline of 10.2 per 1000 to 7.5 per 1000. CONCLUSIONS: Quality improvement methodologies increased the utilization of a standardized SAP within neurology outpatient care centers. The SAP is a simplified tool that allows patients and providers to navigate a complex health care system. The utility of an SAP may potentially extend to minimizing unnecessary ED visits.

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.