Benzodiazepine Initiation Effect on Mortality Among Medicare Beneficiaries Post Acute Ischemic Stroke.

Rationale: Despite guideline warnings, older acute ischemic stroke (AIS) survivors still receive benzodiazepines (BZD) for agitation, insomnia, and anxiety despite being linked to severe adverse effects, such as excessive somnolence and respiratory depression. Due to polypharmacy, drug metabolism, comorbidities, and complications during the sub-acute post-stroke period, older adults are more susceptible to these adverse effects. We examined the impact of receiving BZDs within 30 days post-discharge on survival among older Medicare beneficiaries after an AIS. Methods: Using the Medicare Provider Analysis and Review (MedPAR) dataset, Traditional fee-for-service Medicare (TM) claims, and Part D Prescription Drug Event data, we analyzed a random 20% sample of TM beneficiaries aged 66 years or older who were hospitalized for AIS between July 1, 2016, and December 31, 2019. Eligible beneficiaries were enrolled in Traditional Medicare Parts A, B, and D for at least 12 months before admission. We excluded beneficiaries who were prescribed a BZD within 90 days before hospitalization, passed away during their hospital stay, left against medical advice, or were discharged to institutional post-acute care. Our primary exposure was BZD initiation within 30 days post-discharge, and the primary outcome was 90-day mortality risk differences (RD) from discharge. We followed a trial emulation process involving cloning, weighting, and censoring, plus we used inverse-probability-of-censoring weighting to address confounding. Results: In a sample of 47,421 beneficiaries, 826 (1.74%) initiated BZD within 30 days after discharge from stroke admission or before readmission, whichever occurred first, and 6,392 (13.48%) died within 90 days. Our study sample had a median age of 79, with an inter-quartile range (IQR) of 12, 55.3% female, 82.9% White, 10.1% Black, 1.7% Hispanic, 2.2% Asian, 0.4% American Native, 1.5% Other and 1.1% Unknown. After standardization based on age, sex, race/ethnicity, length of stay in inpatient, and baseline dementia, the estimated 90-day mortality risk was 159 events per 1,000 (95% CI: 155, 166) for the BZD initiation strategy and 133 events per 1,000 (95% CI: 132, 135) for the non-initiation strategy, with an RD of 26 events per 1,000 (95% CI: 22, 33). Subgroup analyses showed RDs of 0 events per 1,000 (95% CI: -4, 11) for patients aged 66-70, 3 events per 1,000 (95% CI: -1, 13) for patients aged 71-75, 10 events per 1,000 (95% CI: 3, 23) for patients aged 76-80, 27 events per 1,000 (95% CI: 21, 46) for patients aged 81-85, and 84 events per 1,000 (95% CI: 73, 106) for patients aged 86 years or older. RDs were 34 events per 1,000 (95% CI: 26, 48) and 20 events per 1,000 (95% CI: 11, 33) for males and females, respectively. RDs were 87 events per 1,000 (95% CI: 63, 112) for patients with baseline dementia and 18 events per 1,000 (95% CI: 13, 21) for patients without baseline dementia. Conclusion: Initiating BZDs within 30 days post-AIS discharge significantly increased the 90-day mortality risk among Medicare beneficiaries aged 76 and older and for those with baseline dementia. These findings underscore the heightened vulnerability of older adults, especially those with cognitive impairment, to the adverse effects of BZDs.

Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.

OBJECTIVES: Monitoring seizure control metrics is key to clinical care of patients with epilepsy. Manually abstracting these metrics from unstructured text in electronic health records (EHR) is laborious. We aimed to abstract the date of last seizure and seizure frequency from clinical notes of patients with epilepsy using natural language processing (NLP). METHODS: We extracted seizure control metrics from notes of patients seen in epilepsy clinics from two hospitals in Boston. Extraction was performed with the pretrained model RoBERTa_for_seizureFrequency_QA, for both date of last seizure and seizure frequency, combined with regular expressions. We designed the algorithm to categorize the timing of last seizure ("today", "1-6 days ago", "1-4 weeks ago", "more than 1-3 months ago", "more than 3-6 months ago", "more than 6-12 months ago", "more than 1-2 years ago", "more than 2 years ago") and seizure frequency ("innumerable", "multiple", "daily", "weekly", "monthly", "once per year", "less than once per year"). Our ground truth consisted of structured questionnaires filled out by physicians. Model performance was measured using the areas under the receiving operating characteristic curve (AUROC) and precision recall curve (AUPRC) for categorical labels, and median absolute error (MAE) for ordinal labels, with 95 % confidence intervals (CI) estimated via bootstrapping. RESULTS: Our cohort included 1773 adult patients with a total of 5658 visits with reported seizure control metrics, seen in epilepsy clinics between December 2018 and May 2022. The cohort average age was 42 years old, the majority were female (57 %), White (81 %) and non-Hispanic (85 %). The models achieved an MAE (95 % CI) for date of last seizure of 4 (4.00-4.86) weeks, and for seizure frequency of 0.02 (0.02-0.02) seizures per day. CONCLUSIONS: Our NLP approach demonstrates that the extraction of seizure control metrics from EHR is feasible allowing for large-scale EHR research.

Emergency department utilization among adults with epilepsy: A multi-state cross-sectional analysis, 2010-2019.

OBJECTIVE: We described patterns and trends in ED use among adults with epilepsy in the United States. METHODS: Utilizing inpatient and ED discharge data from seven states, we conducted a cross-sectional analysis to identify adult ED visits diagnosed with epilepsy or seizures from 2010 to 2019. Using ED visit counts and estimates of state-level epilepsy prevalence, we calculated ED visit rates overall and by payer, condition, and year. RESULTS: Our data captured 304,935 ED visits with epilepsy as a primary or secondary diagnosis in 2019. Across the seven states, visit rates ranged between 366 and 726 per 1000 and were higher than rates for adults without epilepsy in all states but one. ED visit rates were highest among Medicare and Medicaid beneficiaries (vs commercial or self-pay). Adults with epilepsy were more likely to be admitted as inpatients. Visits for nervous system disorders were 6.3-8.2 times higher among people with epilepsy, and visits for mental health conditions were 1.2-2.6 times higher. Increases in ED visit rates from 2010 to 2019 among people with epilepsy exceeded increases among adults without by 6.0-27.3 percentage points. CONCLUSION: Adults with epilepsy visit the ED frequently and visit rates have been increasing over time. These results underscore the importance of identifying factors contributing to ED use and designing tailored interventions to improve ambulatory care quality.

Centromedian region thalamic responsive neurostimulation mitigates idiopathic generalized and multifocal epilepsy with focal to bilateral tonic-clonic seizures.

OBJECTIVE: Although >30% of epilepsy patients have drug-resistant epilepsy (DRE), typically those with generalized or multifocal disease have not traditionally been considered surgical candidates. Responsive neurostimulation (RNS) of the centromedian (CM) region of the thalamus now appears to be a promising therapeutic option for this patient population. We present outcomes following CM RNS for 13 patients with idiopathic generalized epilepsy (IGE) and eight with multifocal onsets that rapidly generalize to bilateral tonic-clonic (focal to bilateral tonic-clonic [FBTC]) seizures. METHODS: A retrospective review of all patients undergoing bilateral CM RNS by the senior author through July 2022 were reviewed. Electrodes were localized and volumes of tissue activation were modeled in Lead-DBS. Changes in patient seizure frequency were extracted from electronic medical records. RESULTS: Twenty-one patients with DRE underwent bilateral CM RNS implantation. For 17 patients with at least 1 year of postimplantation follow-up, average seizure reduction from preoperative baseline was 82.6% (SD = 19.0%, median = 91.7%), with 18% of patients Engel class 1, 29% Engel class 2, 53% Engel class 3, and 0% Engel class 4. There was a trend for average seizure reduction to be greater for patients with nonlesional FBTC seizures than for other patients. For patients achieving at least Engel class 3 outcome, median time to worthwhile seizure reduction was 203.5 days (interquartile range = 110.5-343.75 days). Patients with IGE with myoclonic seizures had a significantly shorter time to worthwhile seizure reduction than other patients. The surgical targeting strategy evolved after the first four subjects to achieve greater anatomic accuracy. SIGNIFICANCE: Patients with both primary and rapidly generalized epilepsy who underwent CM RNS experienced substantial seizure relief. Subsets of these patient populations may particularly benefit from CM RNS. The refinement of lead targeting, tuning of RNS system parameters, and patient selection are ongoing areas of investigation.

Automated Identification of Fall-related Injuries in Unstructured Clinical Notes.

Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated Natural Language Processing (NLP) models to address this issue. We utilized all available clinical notes from the Mass General Brigham for 2,100 older adults, identifying 154,949 paragraphs of interest through automatic scanning for FRI-related keywords. Two clinical experts directly labeled 5,000 paragraphs to generate benchmark-standard labels, while 3,689 validated patterns were annotated, indirectly labeling 93,157 paragraphs as validated-standard labels. Five NLP models, including vanilla BERT, RoBERTa, Clinical-BERT, Distil-BERT, and SVM, were trained using 2,000 benchmark paragraphs and all validated paragraphs. BERT-based models were trained in three stages: Masked Language Modeling, General Boolean Question Answering (QA), and QA for FRI. For validation, 500 benchmark paragraphs were used, and the remaining 2,500 for testing. Performance metrics (precision, recall, F1 scores, Area Under ROC [AUROC] or Precision-Recall [AUPR] curves) were employed by comparison, with RoBERTa showing the best performance. Precision was 0.90 [0.88-0.91], recall [0.90-0.93], F1 score 0.90 [0.89-0.92], AUROC and AUPR curves of 0.96 [0.95-0.97]. These NLP models accurately identify FRIs from unstructured clinical notes, potentially enhancing clinical notes-based research efficiency.

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.

Enhancing Value and Well-Being: The Basket of Motivators Framework for Aligning Neurology Clinical Practices With Performance Outcomes.

Purpose of Review: Physician burnout, which is prevalent in neurology, has accelerated in recent years. While multifactorial, a major contributing factor to burnout is a payment model that rewards volume over quality, leaving physicians overburdened and unfulfilled. The aim of this review was to investigate ways of reducing burnout while improving quality-based outcomes in a value-based health care model. Recent Findings: Burnout affects researchers, educators, clinicians, and administrators in all fields and tracks, but neurologists experience some of the worst burnout rates among specialties. Transitioning to a value-based health care model, which rewards quality and outcomes over volume, may contribute to reversing the burnout trend. However, this requires that physicians feel valued in the workplace in ways corresponding to their preferences. We propose to stratify neurologists using the "basket of motivators" framework, which operates multiple individual-based and team-based motivators including balance among work responsibilities, work-life balance, institutional pride, self-actualization at work, work environment, and finances. By tailoring individual-based and team-based financial and nonfinancial incentives, neurologists are empowered to work at the top of their license to provide high-impact clinical care while combating the most prominent causes of burnout. Summary: To address the neurologist burnout epidemic, a transition to value-based health care is needed that rewards quality-based performance outcomes through both individual-based and team-based approaches that apply financial and nonfinancial incentives. Understanding the underlying motivations behind neurologists' drives to work can inform tailored incentives that allow neurologists to provide value to their patients and feel valued by their organizations.

Benzodiazepine Initiation and the Risk of Falls or Fall-Related Injuries in Older Adults Following Acute Ischemic Stroke.

Background: Benzodiazepine use in older adults following acute ischemic stroke (AIS) is common, yet short-term safety concerning falls or fall-related injuries remains unexplored. Methods: We emulated a hypothetical randomized trial of benzodiazepine use during the acute post stroke recovery period to assess incidence of falls or fall related injuries in older adults. Using linked data from the Get With the Guidelines Registry and Mass General Brigham's electronic health records, we selected patients aged 65 and older admitted for Acute Ischemic Stroke (AIS) between 2014 and 2021 with no documented prior stroke and no benzodiazepine prescriptions in the previous 3 months. Potential for immortal-time and confounding biases was addressed via separate inverse-probability weighting strategies. Results: The study included 495 patients who initiated inpatient benzodiazepines within three days of admission and 2,564 who did not. After standardization, the estimated 10-day risk of falls or fall-related injuries was 694 events per 1000 (95% confidence interval CI: 676-709) for the benzodiazepine initiation strategy and 584 events per 1000 (95% CI: 575-595) for the non-initiation strategy. Subgroup analyses showed risk differences of 142 events per 1000 (95% CI: 111-165) and 85 events per 1000 (95% CI: 64-107) for patients aged 65 to 74 years and for those aged 75 years or older, respectively. Risk differences were 187 events per 1000 (95% CI: 159-206) for patients with minor (NIHSS≤ 4) AIS and 32 events per 1000 (95% CI: 10-58) for those with moderate-to-severe AIS. Conclusions: Initiating inpatient benzodiazepines within three days of AIS is associated with an elevated 10-day risk of falls or fall-related injuries, particularly for patients aged 65 to 74 years and for those with minor strokes. This underscores the need for caution with benzodiazepines, especially among individuals likely to be ambulatory during the acute and sub-acute post-stroke period.

Transforming Value-Based Dementia Care-Implications for the GUIDE Model.

This Viewpoint reviews the Guiding an Improved Dementia Experience (GUIDE) Model to assess its suitability in providing equitable and cost-effective dementia care and to compare it with previously introduced specialty care payment models to identify opportunities for refining payment innovation in dementia care.

Multifaceted Strategy Based on Automated Text Messaging After a Recent Heart Failure Admission: The MESSAGE-HF Randomized Clinical Trial.

Importance: Readmissions after an index heart failure (HF) hospitalization are a major contemporary health care problem. Objective: To evaluate the feasibility and efficacy of an intensive telemonitoring strategy in the vulnerable period after an HF hospitalization. Design, Setting, and Participants: This randomized clinical trial was conducted in 30 HF clinics in Brazil. Patients with left ventricular ejection fraction less than 40% and access to mobile phones were enrolled up to 30 days after an HF admission. Data were collected from July 2019 to July 2022. Intervention: Participants were randomly assigned to a telemonitoring strategy or standard care. The telemonitoring group received 4 daily short message service text messages to optimize self-care, active engagement, and early intervention. Red flags based on feedback messages triggered automatic diuretic adjustment and/or a telephone call from the health care team. Main Outcomes and Measures: The primary end point was change in N-terminal pro-brain natriuretic peptide (NT-proBNP) from baseline to 180 days. A hierarchical win-ratio analysis incorporating blindly adjudicated clinical events (cardiovascular deaths and HF hospitalization) and variation in NT-proBNP was also performed. Results: Of 699 included patients, 460 (65.8%) were male, and the mean (SD) age was 61.2 (14.5) years. A total of 352 patients were randomly assigned to the telemonitoring strategy and 347 to standard care. Satisfaction with the telemonitoring strategy was excellent (net promoting score at 180 days, 78.5). HF self-care increased significantly in the telemonitoring group compared with the standard care group (score difference at 30 days, -2.21; 95% CI, -3.67 to -0.74; P = .001; score difference at 180 days, -2.08; 95% CI, -3.59 to -0.57; P = .004). Variation of NT-proBNP was similar in the telemonitoring group compared with the standard care group (telemonitoring: baseline, 2593 pg/mL; 95% CI, 2314-2923; 180 days, 1313 pg/mL; 95% CI, 1117-1543; standard care: baseline, 2396 pg/mL; 95% CI, 2122-2721; 180 days, 1319 pg/mL; 95% CI, 1114-1564; ratio of change, 0.92; 95% CI, 0.77-1.11; P = .39). Hierarchical analysis of the composite outcome demonstrated a similar number of wins in both groups (telemonitoring, 49 883 of 122 144 comparisons [40.8%]; standard care, 48 034 of 122 144 comparisons [39.3%]; win ratio, 1.04; 95% CI, 0.86-1.26). Conclusions and Relevance: An intensive telemonitoring strategy applied in the vulnerable period after an HF admission was feasible, well-accepted, and increased scores of HF self-care but did not translate to reductions in NT-proBNP levels nor improvement in a composite hierarchical clinical outcome. Trial Registration: ClinicalTrials.gov Identifier: NCT04062461.

Regionality in vector control: effect of fluctuating temperature in the susceptibility of Aedes aegypti (Diptera: Culicidae) larvae to Pyriproxyfen.

Using Pyriproxyfen in controlling Aedes aegypti shows great potential considering its high competence in low dosages. As an endocrine disruptor, temperature can interfere with its efficiency, related to a decrease in larval emergence inhibition in hotter environments. However, previous studies have been performed at constant temperatures in the laboratory, which may not precisely reflect the environmental conditions in the field. The aim of this study was to assess the effect of the fluctuating temperatures in Pyriproxyfen efficiency on controlling Aedes aegypti larvae. We selected maximum and minimum temperatures from the Brazilian Meteorological Institute database from September to April for cities grouped by five regions. Five fluctuating temperatures (17-26; 20-28.5; 23-32.5; 23-30.5; 19.5-31 °C) were applied to bioassays assessing Pyriproxyfen efficiency in preventing adult emergence in Aedes aegypti larvae in five concentrations. In the lowest temperatures, the most diluted Pyriproxyfen treatment (0.0025 mg/L) was efficient in preventing the emergence of almost thrice the larvae than in the hottest temperatures (61% and 21%, respectively, p value = 0.00015). The concentration that inhibits the emergence of 50% of the population was lower than that preconized by the World Health Organization (0.01 mg/L) in all treatments, except for the hottest temperatures, for which we estimated 0.010 mg/L. We concluded that fluctuating temperatures in laboratory bioassays can provide a more realistic result to integrate the strategies in vector surveillance. For a country with continental proportions such as Brazil, considering regionalities is crucial to the rational use of insecticides.

Real-World Continuous EEG Utilization and Outcomes in Hospitalized Patients With Acute Cerebrovascular Diseases.

PURPOSE: Continuous electroencephalography (cEEG) is recommended for hospitalized patients with cerebrovascular diseases and suspected seizures or unexplained neurologic decline. We sought to (1) identify areas of practice variation in cEEG utilization, (2) determine predictors of cEEG utilization, (3) evaluate whether cEEG utilization is associated with outcomes in patients with cerebrovascular diseases. METHODS: This cohort study of the Premier Healthcare Database (2014-2020), included hospitalized patients age >18 years with cerebrovascular diseases (identified by ICD codes). Continuous electroencephalography was identified by International Classification of Diseases (ICD)/Current Procedural Terminology (CPT) codes. Multivariable lasso logistic regression was used to identify predictors of cEEG utilization and in-hospital mortality. Propensity score-matched analysis was performed to determine the relation between cEEG use and mortality. RESULTS: 1,179,471 admissions were included; 16,777 (1.4%) underwent cEEG. Total number of cEEGs increased by 364% over 5 years (average 32%/year). On multivariable analysis, top five predictors of cEEG use included seizure diagnosis, hospitals with >500 beds, regions Northeast and South, and anesthetic use. Top predictors of mortality included use of mechanical ventilation, vasopressors, anesthetics, antiseizure medications, and age. Propensity analysis showed that cEEG was associated with lower in-hospital mortality (Average Treatment Effect -0.015 [95% confidence interval -0.028 to -0.003], Odds ratio 0.746 [95% confidence interval, 0.618-0.900]). CONCLUSIONS: There has been a national increase in cEEG utilization for hospitalized patients with cerebrovascular diseases, with practice variation. cEEG utilization was associated with lower in-hospital mortality. Larger comparative studies of cEEG-guided treatments are indicated to inform best practices, guide policy changes for increased access, and create guidelines on triaging and transferring patients to centers with cEEG capability.

Brain health scores to predict neurological outcomes from electronic health records.

BACKGROUND: Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits. METHODS: This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021. Survival analysis with an 11-year follow-up period was performed to predict the risk of intracranial hemorrhage, ischemic stroke, depression, death and composite outcome of dementia, Alzheimer's disease, and mild cognitive impairment. Variables included age, sex, vital signs, laboratory values, employment status and social covariates pertaining to marital, tobacco and alcohol status. Random sampling was performed to create a training (70%) set for hyperparameter tuning in internal 5-fold cross validation and an external hold-out testing (30%) set of patients, both stratified by age. Risk ratios for high and low risk groups were evaluated in the hold-out test set, using 1000 bootstrapping iterations to calculate 95% confidence intervals (CI). RESULTS: The cohort comprised 17,040 patients with an average age of 49 ± 15.6 years; majority were males (57 %), White (78 %) and non-Hispanic (80 %). The low and high groups average risk ratios [95 % CI] were: intracranial hemorrhage 0.46 [0.45-0.48] and 2.07 [1.95-2.20], ischemic stroke 0.57 [0.57-0.59] and 1.64 [1.52-1.69], depression 0.68 [0.39-0.74] and 1.29 [0.78-1.38], composite of dementia 0.27 [0.26-0.28] and 3.52 [3.18-3.81] and death 0.24 [0.24-0.24] and 3.96 [3.91-4.00]. CONCLUSIONS: Simple risk scores derived from routinely collected EHR accurately quantify the risk of developing common neurologic and psychiatric diseases. These scores can be computed automatically, prior to medical care visits, and may thus be useful for large-scale brain health screening.

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.

Improving Seizure Frequency Documentation and Classification.

Background and Objectives: Accurate and reliable seizure data are essential for evaluating treatment strategies and tracking the quality of care in epilepsy clinics. This quality improvement project aimed to increase seizure documentation (i.e., documentation of seizure frequency from 80% to 100%, date of last seizure from 35% to 50%, and International League Against Epilepsy (ILAE) seizure classification from 35% to at least 50%) over 6 months. Methods: We surveyed 7 epileptologists to determine their perceived seizure frequency, ILAE classification, and date of last seizure documentation habits. Baseline data were collected weekly from September to December 2021. Subsequently, we implemented a newly created flowsheet in our Electronic Health Record (EHR) based on the Epilepsy Learning Healthcare System (ELHS) Case Report Forms to increase seizure documentation in a standardized way. Two epileptologists tested this flowsheet tool in their epilepsy clinics between February 2022 and July 2022. Data were collected weekly and compared with documentation from other epileptologists within the same group. Results: Epileptologists at our center believed they documented seizure frequency for 84%-87% of clinic visits, which aligned with baseline data collection, showing they recorded seizure frequency for 83% of clinic visits. Epileptologists believed they documented ILAE classification for 47%-52% of clinic visits, and baseline data showed this was documented in 33% of clinic visits. They also reported documenting the date of the last seizure for 52%-63% of clinic visits, but this occurred in only 35% of clinic visits. After implementing the new flowsheet, documentation increased to nearly 100% for all fields being completed by the providers who tested the flowsheet. Discussion: We demonstrated that by implementing an easy-to-use standardized EHR documentation tool, our documentation of critical metrics, as defined by the ELHS, improved dramatically. This shows that simple and practical interventions can substantially improve clinically meaningful documentation.