Improving Seizure Frequency Documentation and Classification

Abstract

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.

Introduction

Epilepsy is a chronic neurologic disorder that affects millions of individuals worldwide. People with epilepsy (PWE) face many challenges, continually underscoring the importance of finding ways to improve standards of epilepsy care and meaningfully improve quality of life.¹ To quantify and evaluate the quality of care, epilepsy quality measures were developed.^2-5 However, one of the most significant hurdles in measuring the quality of care in this condition is the inconsistent and unstructured provider documentation of seizure frequency and classification in electronic health records (EHRs).^3-6

Medical decision-making in epilepsy care relies on knowledge of seizure types and frequency—these can be considered vital signs for patients with epilepsy. Documentation of these parameters is critical for tracking response to treatment and long-term outcomes. Variability in documentation habits of health care professionals makes longitudinal tracking of these metrics (and the outcomes tied to them) challenging outside the context of clinical trials or research studies. Health care providers often use free-form, unstructured text when describing patient care in EHRs. This can lead to inaccurate seizure frequency tracking over time, resulting in decreased accuracy of outcome assessment and treatment responses.

In addition, a lack of standardized medical records can impede seizure frequency documentation, making it difficult to track and evaluate treatment outcomes that directly affect care.^6-13 To improve the accuracy and effectiveness of patient care, health care providers must implement standardized documentation in clinical practice, both in the inpatient and outpatient settings, such as proper seizure classification. This is an essential quality metric that affects medical decision-making. As the current standard, International League Against Epilepsy (ILAE) classification is initially determined by observing specific symptoms or signs; it is then based on clinical presentation and occasionally on electroencephalography (EEG), imaging, or laboratory studies.¹⁴ ILAE classification is critical for communication, patient care, and research consistency.^15,16

Standardizing seizure frequency documentation within EHRs and simplifying data extraction methods is possible. Accurately tracking such data in the EHR can also make tracking metrics related to the quality of life, disability-adjusted life years, and healthcare utilization easier.¹⁷ Frequency and types of seizures can reveal crucial details regarding how effectively a patient is being treated and how well they adhere to their prescribed treatment plan. In addition, tracking seizure frequency can be used to assess the impact of seizures on a patient's quality of life and is an essential metric for monitoring responses to treatment, treatment adjustment, order testing, and monitoring for indirect consequences of seizures.

We aimed to assess whether implementing a simple tool within the EHR for seizure frequency documentation and classification would lead to sustained improvement in documentation over 6 months.

Methods

We conducted a QI project from September 2021 to July 2022 at our tertiary care facility (i.e., Colorado Anschutz Medical Campus) as part of the Epilepsy Learning Healthcare System (ELHS) QI initiative. ELHS is a patient-centered network of academic hospitals, patient/family partner (PFP) representatives, and nonprofit organizations.¹⁸ The network aims to improve health care delivery processes and patient outcomes through data collection and iterative interventions.¹⁸ This article was prepared by the standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 guidelines¹⁹ and was organized using relevant categories. However, it should be noted that only some of the recommended types were incorporated as per the guidelines.

Eligible patient encounters included all adult patients aged 18 years or older seen by epileptologists for routine care at our epilepsy clinic from September 2021 to July 2022. The 7 participating epileptologists each see an average of 50 patients with suspected or diagnosed epilepsy for monthly clinical care. The visit frequency varies given the level of seizure control and visit type (e.g., new patient vs established).

Quality improvement projects use a standardized methodology to demonstrate changes related to interventions.²⁰ Each project begins with a SMART Aim in which states what is going to be improved, by how much, and by when are clearly stated. The next step is creation of a Key Driver Diagram in which the drivers of the change and potential interventions for each driver are specified.

In our project, a team of experts in epilepsy and QI held weekly huddles from September 2021 through early February to design the data entry flowsheet for adult patients. The team developed a key driver diagram (Figure 1) aimed at improving the documentation rate of seizure frequency from 80% to 100%, seizure type from 35% to 50%, and date of late seizure from 35% to 50% in the epilepsy clinics and sustaining the increase for 3 months. We crafted our Specific Aims for this quality improvement project by considering 6 overarching “Aims for Improvement”²¹ commonly used to guide quality improvement work in health care. We chose clear, numerical goals, which we believed could be achievable safely and effectively over a well-defined period, which would help improve the quality of documentation for key metrics importing for the clinical care of people with epilepsy. The aim to achieve 100% documentation of seizure frequency is based on the importance of this in clinical management for people with epilepsy—we believe this metric can be considered a vital sign for people with epilepsy and is critical in assessing the safety and efficacy of antiseizure therapy. The aims to improve documentation of seizure classification and date of last seizure by 15% over 3 months were chosen as a meaningful and achievable improvement over that period, and a more modest improvement goal was set due to the low rates of documenting these metrics in free-form clinic notes at baseline, knowing this may require additional PDSA cycles to continue working on improving these metrics in the future. Interventions consisting of crucial elements were then developed for each driver and sequentially implemented.

Figure 1. Key Driver Diagram.

Summary of key drivers and interventions for successfully incorporating the flowsheet into routine clinical practice.

The initial survey on perceived documentation (i.e., seizure frequency, ILAE classification, and date of last seizure) is ELHS network–wide agreed-upon quality measures, which parallel the American Academy of Neurology's Epilepsy and Inpatient Neurology quality measures for accurate measurement of seizure type and frequency and defining numerator and denominator populations to report the percentage of patients meeting the measure.^7,22 EHR data from clinic appointments for all 7 epileptologists were abstracted weekly to supplement the subjective data. After this, an educational session was conducted to demonstrate the results of subjective data from a survey to interpret the need for better documentation. The data entry flowsheet was designed and built in Epic with the help of a certified Epic Physician Builder.

The flowsheet incorporates the most updated ILAE seizure classification as the first quality measure,¹⁴ ensuring standardized classification across sites. The thresholds used for seizure frequencies and dates of last seizure were network-wide thresholds for ELHS, which have been proposed to ensure comparable measures across sites. Diagnostic certainty was likewise a common ELHS measure that we incorporated into the flowsheet to assess the level of evidence that health care providers believe they have obtained regarding a patient's diagnosis (e.g., video-EEG confirmed seizure would be considered “definite” vs a purely clinical diagnosis (no abnormalities on imaging or interictal EEGs) and a good response to antiseizure medications may be considered “probable”) (eAppendix 1, links.lww.com/CPJ/A474).

Within the EHR at our institution, the flowsheet exists in the Charting tab, which epileptologists open during the clinical encounters and can fill in the information by clicking on the appropriate answers next to each prompt either during their discussion with the patient or afterward before closing the encounter. This stores the information for each visit, which can be carried forward and modified for subsequent encounters. Epileptologists can also use commands (“dot phrases”) to automatically pull this information into their clinic notes, though the documentation still exists for each clinical encounter once it is entered in the Charting tab whether a provider chooses to insert it directly into their clinic note or not. The epileptologists involved in our intervention chose to automatically include all Charting tab information in the appropriate sections of their clinic notes, saving time and reducing clinical documentation redundancy. Furthermore, it was more accessible to abstract data from Epic by creating the flowsheet because weekly queries could be run into Epic to automatically retrieve data without requiring manual chart review. The flowsheet was implemented following the baseline data collection period from September 2021 to December 2021 by 2 epileptologists. We chose to proceed with a small test of change with only 2 epileptologists before determining efficacy and scaling its use to the whole group. To assess the impact of flowsheet on documentation rates, we tracked seizure documentation weekly and compared documentation rates between the epileptologists who used the flowsheet and the epileptologists who did not use the flowsheet (as a comparison group) from February 2022 to April 2022. The study was continued for another 3 months (May 2022–July 2022) with flowsheet users to assess the documentation of the study parameters (seizure frequency, date of last seizure, and ILAE classification documentation). Among the 7 providers in the study, the 2 epileptologists who used the flowsheet saw, on average, 10 outpatients with suspected or diagnosed epilepsy per week. Finally, we conducted another educational session showing the results of the newly implemented smartlist and the use of the seizure-focused flowsheet.

The process measure used in the study was the eligible patient encounters in the clinic (denominator) who had documentation of seizure frequency, ILAE classification, and date of last seizure (numerator) from September 2021 to July 2022.

Analysis

Data were manually collected in Microsoft Excel weekly. We tracked process changes over time by analyzing data using statistical process control (SPC) charts. Data are displayed as a time series with the outcome on the y-axis and time on the x-axis (i.e., a control chart). Arrows are used to indicate when an intervention has occurred. The control chart consists of a centerline and control limits. The centerline represents the average value of the data across time points, while the control limits are defined by the standard deviation of the data. The upper control limit (UCL) represents the highest acceptable value or boundary, which can be thought of as 3 standard deviations above the mean, and the lower control limit (LCL) represents the lowest acceptable value or boundary for a process parameter in a control chart. These control limits help identify whether the process exhibits any special cause variation.²³ Quality improvement uses SPC charts to show whether a change brought by an intervention is significant and statistically valid. There are a variety of tools to assess the significance of change demonstrated by an SPC chart including a single point) going above or below the upper and lower control limits, 6 consecutive points increasing or decreasing, 2 of 3 points near the outer one-third of a control limit, and 15 consecutive points on the inner one-third of the chart.²³

The centerline and control limits were adjusted using the American Society for Quality (ASQ) criteria,²⁰ and our results were assessed weekly. Different criteria can be used to determine whether a centerline shift has taken place. P charts were used to display the percentage of documentation of seizure frequency, ILAE classification, and date of last seizure by the epileptologists in the clinics weekly.

Standard Protocol Approvals, Registrations, and Patient Consents

This study was IRB approved. Patient consent was waived because of the QI nature of this initiative. We confirm that we have read the Journal's position on ethical publication issues and that this report adheres to those guidelines.

Data Availability

After IRB approval and data use agreements between institutions, data can be available to qualified researchers.

Results

Epileptologists at our center believed they documented seizure frequency for 84%–87% of clinic visits on the initial survey. This aligned with the subsequent baseline data collection showing they recorded seizure frequency for 83% of clinic visits. However, epileptologists subjectively believed they documented ILAE classification for 47%–52% of clinic visits on the initial survey, with subsequent baseline data showing this was written in 33% of clinic visits. They also estimated documenting the date of the last seizure for 52%–63% of clinic visits, though this could be verified in only 35% of clinic visits during the baseline period (Table). Two of the 7 epileptologists in our study implemented the flowsheet in their clinic workflow, subsequently increasing their documentation to nearly 100% for all fields. The epileptologists who did not use the flowsheet were also monitored during this time as a comparison group. During this period, the seizure frequency documentation was between 75% and 90% in clinics without the flowsheet, while documentation was 100% in clinics with the flowsheet. ILAE classification documentation was 22%–40% in clinics without the flowsheet and 100% in clinics with the flowsheet. Documentation of the date of last seizure was 30%–76% in clinics without the flowsheet and 100% in clinics with the flowsheet. The study was continued for another 3 months in clinics with a flowsheet to assess for sustainability and found that documentation continued at 100% with the use of a flowsheet for that period.

Table.

Seizure Documentation

Quality measures	Seizure frequency documentation	ILAE classification documentation	Date of last seizure documentation
Subjective survey	84%–87%	47%–52%	52%–63%
Objective baseline data	83%	33%	35%
Intervention with flowsheet	100%	100%	100%
Intervention without flowsheet	75%	25%	36%

The P chart (Figure 2) on documentation of seizure frequency shows a centerline shift in February 2022, moving the baseline average from 70% to 100%. Similarly, documentation of ILAE classification and date of last seizure saw a positive shift from 35% to 100% (Figure 2) and 36%–100% (Figure 2), respectively, for the 2 epileptologists who implemented the flowsheet. However, the documentation patterns for the epileptologists who did not incorporate the flowsheet into their clinic remained the same. The baseline documentation frequency for all 3 parameters nearly increased to 100% because there was a centerline shift in February 2022 for the 2 epileptologists who used the flowsheet.

Figure 2. Graphical Representation of Documentation of Seizure Frequency, ILAE Classification, and Date of Last Seizure (Rows).

The charts in the left column are those for education-only intervention group, whereas the charts in the right column are the her intervention group. Lines representing upper (orange) and lower (gray) confidence limits surround the average (blue dashed centerline) of the weekly completion rates. Annotations describe the weeks in which interventions occurred, and the red arrow indicates the week in which there was a midline shift.

To determine whether the observed changes were due to common cause variation (that is, an expected amount of change within the normal patterns of interpretation of the process) or were scheduled to particular cause variation (a nonrandom departure from the usual pattern), SPC rules were applied. The 8-point rule was used to assess the stability of the process over time.²⁴ The centerline shift observed in the documentation of seizure frequency, ILAE classification, and the date of the last seizure after the implementation of the flowsheet was considered significant because it was sustained for at least 8 consecutive data points, indicating a transition to a stable process at a higher rate of documentation. By contrast, the documentation for the epileptologists who did not incorporate the flowsheet remained the same. Overall, the results suggest that the intervention by implementing the flowsheet was effective in improving documentation patterns and was statistically significant. SPC rules provide a valuable tool for assessing the effectiveness and sustainability of quality improvement interventions.

Discussion

There is growing evidence that implementing standardized data collection at the point of care for patients with epilepsy is essential and sustainable. In this quality improvement project, we successfully implemented a process for structured epileptologist documentation among a large sample of adult patients seen in a tertiary epilepsy clinic for routine clinical care. We achieved a mean weekly standardized documentation rate of 100% using a simple and interactive EHR tool that mirrored an evidence-based documentation template (the ELHS CRF for provider-reported seizure documentation) and a series of other interventions. Feedback from the 2 providers who tested the flowsheet indicated their willingness to continue using it in their practice. They found the tool helpful in standardizing seizure documentation, improving workflow, and did not consider it burdensome in their daily practice. Given the positive feedback and demonstrated efficacy, the flowsheet will be offered to the other providers in the practice now that we have proven its efficacy in increasing and sustaining seizure documentation rate. It is worth noting that the additional time spent for documentation per encounter ranged between 2 and 5 minutes (new vs established). This relatively small time investment demonstrates that the implementation of the EHR flowsheet did not significantly increase the overall burden on providers, further supporting its feasibility and acceptability in clinical practice.

The implementation of standardized documentation through the EHR flowsheet directly correlates with the goals of the ELHS, a larger collaborative initiative dedicated to advancing epilepsy care and quality improvement. Standardized documentation is crucial for enabling subsequent goals of the ELHS, which include enhancing the quality of patient care, improving patient outcomes, and fostering continuous improvement in epilepsy care.²⁵ By creating the EHR flowsheet, we contribute to the standardized data collection within the ELHS network, ensuring consistency and comparability of documentation across different health care settings. Standardized documentation is considered core to the data submission in other learning health care systems such as ImproveCareNow²⁶ and Solutions for Patients Safety.²⁷ The improvement in outcomes and publications from these LHS were all based on standardized data entry.

The American Academy of Neurology (AAN) identified a lack of widespread quality measures specifically targeted to PWE in 2011 and initiated the standardization of care for PWE.²⁸ This included observation of 8 quality measures: determination of seizure type/frequency, etiology and review/order of EEG, neuroimaging studies, surgical referrals, counseling about side effects of drugs, safety issues, and reproductive health. Since then, there has been variable adherence to the above-stated quality measures reported by survey studies among neurologists.²⁹ In our study, the implementation of an EHR flowsheet increased documentation of seizure frequency, date of last seizure, and ILAE classification to 100% among those who adopted the use of the flowsheet compared with no change among those who were only made aware that documenting these metrics were essential and were being monitored. However, we observed a decline in the documentation of ILAE classification among those who did not use the flowsheet over time, indicating that their initial improvement at the beginning of the study was likely due to increased awareness and attention drawn to documentation during the survey period, because they did not have the flowsheet, so documenting as they had with no additional encouragement resulted in decreased documentation. This decline in documentation highlights the importance of implementing tools such as EHR flowsheet to maintain consistent adherence to quality measures.

Epileptologists and other health care providers who are taking care of people with epilepsy should be familiar with the most updated seizure classification. It is important for both the healthcare providers and patients to be familiar with seizure types. Seizure classification is a common metric across ELHS sites and was an agreed-upon metric for data collection, as it plays a crucial role in patient awareness, treatment decisions, monitoring for treatment efficacy, and clinical research. In general, baseline seizure classification among providers in clinic notes has been highly variable and often lacks detail for full ILAE classification. However, there is high inter-rater agreement in the ILAE classification of drug resistance,³⁰ though there has not been a prospective study evaluating this for all ILAE seizure classifications. It is important to note that this classification is most often a clinical diagnosis that relies on careful history taking and synthesis of diagnostic testing, and the purpose of including this in our flowsheet is to improve and standardize documentation of seizure types at our center and therefore also across centers through the ELHS network. Because precise classification is not always possible based on available information, there is a selection in the flowsheet for “Unknown onset” (within the epileptic seizures component) or “Nonepileptic or uncertain events” in a separate part of the flowsheet (see eAppendix, links.lww.com/CPJ/A474). As the flowsheet is carried forward and modified on subsequent visits, seizure classification and diagnostic certainty can be updated because further diagnostic information is obtained over time for patients. This functionality of the flowsheet can serve as a reminder and aid in the ongoing evaluation and diagnosis of epilepsy type for patients. Overall, this quality improvement project illustrates a practical approach to improving AAN quality metrics using an EHR flowsheet by epileptologists.

Poorly designed EHRs with complex navigation can significantly prolong the time required for documentation.^31,32 However, tool creation within EHRs is one method for standardizing quality improvement measures and enables documentation of quality measures in people with epilepsy at all levels of care. Standardizing documentation within EHRs supports adherence to best practices through note writing, hardwiring, and data analysis.³³ Development of EHR tools within ELHS supports rapid dissemination of quality improvement tools to address gaps in care. Implementing standardized EHR documentation in epilepsy clinics has also shown benefits in clinical research enrollment, aiding in pharmacogenomics profiling and molecular prognostic tests. Another advantage may be improving the implementation of pragmatic trials in epilepsy using a subgroup-based adaptive design.^34,35

The ability to conduct thorough quality assessments has also been hindered recently due to the inconsistency in the documentation and the diverse formats of clinical data recorded within EHRs.^6,9,11 Many health care providers use free text to document care because they believe it allows for a more detailed and expressive account of the patient's episode of care. Furthermore, there is a concern that extensively structured documentation may lead to a partial representation of the patient's overall condition and potentially overlook important details.^6,9,11 Flowsheets like ours allow providers to use a structured data format incorporating coded information within predefined fields integrated into their free-text notes. This methodology facilitates the convenient identification and retrieval of the coded data within the notes, distinguishing it from free text. Thus, using this method, both structured data in the form of flowsheets and written notes can be used as a practical and effective solution. Provider education requires dedicated time, constant training and reminders, and protected additional time for documentation. This method is time-consuming for providers and QI champions imparting those educational modules, not to mention it contributes to physician burnout.

A prospective study looking at percent changes in the documentation and adherence to AAN quality measures before and after implementation of a similar worksheet showed a significant improvement in the documentation of AAN quality measures after implementing the worksheet. After 6 months of this study duration, there was a 10% increase in the documentation of seizure type and 11% in the documentation of seizure frequency.³⁶ Although there was an improvement in seizure frequency documentation, questions were raised regarding the accuracy of patient-reported information.^36,37

Quality improvement projects in epilepsy care have previously shown the effectiveness of implementing appointment systems and patient communication strategies in reducing the no-show rate in epilepsy clinics.³⁸ Building on these findings, our study addresses the gaps in documentation frequency for various metrics and achieves comparable rates of standardized documentation in EHRs by adopting the EHR flowsheet in routine clinical settings, we have demonstrated how interventions can eliminate these gaps and enhance the quality of patient care. Our overall performance is comparable with rates reported by earlier research that used flowsheets as standardized frameworks to enhance standardized documentation in EHRs.^39-41 The results also represent a snapshot of our continuing Quality Improvement program, an infrastructure of change being developed at one center in collaboration with a more extensive learning health care system working together to make parallel changes. Overall, this work reflects a significant shift in epilepsy care toward continual improvement in the quality of patient care that can be sustained over time.

The changes we have integrated within an EHR enabling standardization of documentation related to seizure type and frequency, including last seizure date, will considerably enhance the quality of epilepsy care by allowing accurate monitoring of seizures and tracking of treatment responses, enabling epileptologists to have a more comprehensive understanding of each patient's condition. This information can lead to timely interventions and adjustments to treatment plans, potentially reducing the likelihood of missed appointments and facilitating prompt follow-up for patients experiencing worsening seizure burden. However, further research specifically targeting these outcomes would be necessary to establish a direct correlation. Our initiative can serve as a model for similar EHR-based quality improvement projects, which can be built on and disseminated through learning health care systems. To make a web of networks, we encourage the production of similar data metrics across EHR systems, which can be supported by involvement in learning health care systems.

We are aware of several limitations to our project. First, other institutions may not have the resources to implement our interventions (e.g., EHR building skills, protected staff time, quality improvement knowledge, framework). The resources available to a tertiary educational clinic continue to benefit our quality improvement initiative. As a result, maintaining this project can be difficult, and the generalization of our results to other environments may be limited. In addition, the 2 epileptologists who used the flowsheet were actively involved in ELHS and were early adopters of quality improvement initiatives at the center, which could have biased the discrepancy between the flowsheet and education-only arms. Furthermore, IT resources are needed if manual data abstraction is not conducted. If a center is performing manual work, then a CRC or Clinical/Research Fellows who would be highly engaged in the project to ensure everything is documented and the data collected are reliable may be necessary. Ideally, providers would have QI time built into the initial visit. This would allow for storing the vital information carried forward and updated for each visit. There may be site-specific solutions to improving such documentation, such as involving scribes or additional documentation tools. We recognize that the efficacy of our intervention for increasing standardized documentation does not guarantee an improved standard and continues to require health care provider buy-in for ongoing accurate documentation. However, the quality improvement framework we implemented has successfully maintained provider documentation in a real-world setting for a minimum data collection period.

This QI project has demonstrated the effectiveness of implementing a standardized EHR documentation tool in epilepsy clinics. By implementing the new flowsheet, the project successfully increased the documentation of critical seizure metrics, including seizure frequency, date of last seizure, and ILAE classification, by nearly 100%. Simple and practical interventions can significantly improve clinically meaningful clinical documentation. This intervention is easily integrated into existing EHR systems. It can be replicated in other epilepsy clinics, providing the potential for spread to different contexts. Other clinics and medical centers may face similar challenges in accurately and reliably documenting seizure data, essential for evaluating treatment strategies and tracking the quality of care in epilepsy clinics. This has significant implications for clinical neurology, and practitioners in other areas may benefit from similar documentation tools to improve the accuracy and reliability of data. To further assess its impact, the long-term sustainability of the intervention needs to be monitored and to explore the effect of improved documentation on patient care and outcomes. Future studies should investigate its impact on patient care, physician satisfaction, patient engagement, and health care costs. In conclusion, this project has demonstrated the benefits of high-quality improvement initiatives in health care.

TAKE-HOME POINTS

• → The subjective perception of epileptologists was that they documented seizure frequency for 84%–87%, which aligned with the following baseline data collection showing documentation was 83% of clinic visits. However, it is essential to note that this documentation was not standardized and may only be easily extractable with manual chart review or natural language processing (NLP).

• → Epileptologists believed they are documenting ILAE classification and date of last seizure in 47%–52% and 52%–63% of clinic visits, respectively. Still, the actual baseline data showed this was documented in only 33% and 35% of clinic visits, respectively.

• → Implementing a new electronic health record (EHR) flowsheet significantly improved documentation to nearly 100% for all parameters, including seizure frequency, ILAE classification, and date of last seizure.

• → This study demonstrated that using a standardized EHR flowsheet was a simple and effective intervention for enhancing the accuracy and completeness of clinically relevant documentation in epilepsy clinics.

• → This study continued for an additional 3 months and verified that using the flowsheet maintained 100% documentation of all 3 parameters.

Appendix. Authors

Name	Location	Contribution
Poojith Nuthalapati, MD	Department of Neurology, Massachusetts General Hospital, Harvard Medical School	Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data
Lionel Thomas, MD	Department of Neurology, University of Colorado School of Medicine	Major role in the acquisition of data
Maria A. Donahue, MD	Department of Neurology, Massachusetts General Hospital, Harvard Medical School	Drafting/revision of the article for content, including medical writing for content
Lidia M.V.R. Moura, MD, PhD, MPH	Department of Neurology, Massachusetts General Hospital, Harvard Medical School	Drafting/revision of the article for content, including medical writing for content
Samuel DeStefano, MD	Department of Neurology, University of Colorado School of Medicine	Drafting/revision of the article for content, including medical writing for content
Jennifer R. Simpson, MD	Department of Neurology, University of Colorado School of Medicine	Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data
Jeffrey Buchhalter, MD, PhD	Department of Pediatrics, Cumming School of Medicine, University of Calgary	Drafting/revision of the article for content, including medical writing for content; analysis or interpretation of data
Brandy E. Fureman, PhD	Mission Outcomes Team, Epilepsy Foundation	Drafting/revision of the article for content, including medical writing for content; analysis or interpretation of data
Jacob Pellinen, MD	Department of Neurology, University of Colorado School of Medicine	Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; and analysis or interpretation of data

Study Funding

The authors report no targeted funding.

Disclosure

P. Nuthalapati, M.A. Donahue, S. DeStefano: have no conflict of interest to disclose. L.M.V.R.M.: support from the Centers for Diseases Control and Prevention (U48DP006377), the NIH (NIH-NIA 5K08AG053380-02, NIH-NIA 5R01AG062282-02, NIH-NIA 2P01AG032952-11, NIH- NIA 3R01AG062282-03S1), and the Epilepsy Foundation of America and reports no conflict of interest. J. Pellinen has no conflicts of interest directly related to this work. In the past 2 years, he has received research support from the Department of Neurology at the University of Colorado School of Medicine, the Colorado Clinical and Translational Sciences Institute by way of NIH/NCATS Colorado CTSA Grant Number UL1 TR002535, the NIH/NINDS in the form of a Clinical Research LRP, and from the American Epilepsy Society. He serves as chair of the professional advisory board for the Epilepsy Foundation of Colorado and Wyoming (unpaid), serves as the Epilepsy Section Editor for Current Neurology and Neuroscience Reports, and has received salary support for advisory board work for SK Life Science. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/cp.