Patient headache questionnaires can improve headache diagnosis and treatment in children.

OBJECTIVE: To examine trends in diagnosis of headache and migraine in a large pediatric neurology cohort, and test whether an electronic health record (EHR)-integrated headache questionnaire can increase specificity of diagnosis and likelihood of prescribing migraine treatment. BACKGROUND: Under-diagnosis of migraine contributes to the burden of disease. As we founded our Pediatric Headache Program in 2013, we recognized that the proportion of patients with headache who were given a diagnosis of migraine was much lower than expected. METHODS: We developed a patient headache questionnaire, initially on paper (2013-2014), then in an electronic database (2014-2016), and finally integrated into our electronic health record (pilot: 2016, full: May 2017). We compared diagnoses and prescribed treatments for new patients who were given a headache diagnosis, looking at trends in the proportion of patients given specific diagnoses (migraine, etc.) versus the non-specific diagnosis, "headache." Next, we conducted a prospective cohort study to test for association between provider use of the form and the presence of a specific diagnosis, then for an association between specific diagnosis and prescription of migraine treatment. RESULTS: Between July 2011 and December 2022 the proportion of new headache patients who were given a diagnosis of migraine increased 9.7% and non-specific headache diagnoses decreased 21.0%. In the EHR cohort (June 2017-December 2022, n = 15,122), use of the provider form increased the rate of specific diagnosis to 87.2% (1839/2109) compared to 75.5% (5708/7560) without a patient questionnaire, nearly doubling the odds of making a specific diagnosis (odds ratio [OR] 1.90, 95% confidence interval [CI]: 1.65-2.19). Compared to those given only a non-specific headache diagnosis who were prescribed a migraine therapy 53.7% (1766/3286) of the time, 75.3% (8914/11836) of those given a specific diagnosis received a migraine therapy, more than doubling the odds of prescription (OR 2.39, 95% CI: 2.20-2.60). CONCLUSIONS: Interventions to improve specificity of diagnosis were effective and led to increased rates of prescription of migraine treatments. These results have been sustained over several years. This headache questionnaire was adapted into the Foundation system of EpicCare, so it is broadly available as a clinical and research tool for institutions that use this EHR software.

A quality improvement initiative to improve folic acid supplementation counseling for adolescent females with epilepsy.

OBJECTIVE: We designed a quality improvement (QI) project to improve rates of documented folic acid supplementation counseling for adolescent females with epilepsy, consistent with a quality measure from the American Academy of Neurology and American Epilepsy Society. Our SMART aim was to increase the percentage of visits at which folic acid counseling was addressed from our baseline rate of 23% to 50% by July 1, 2020. METHODS: This initiative was conducted in female patients ≥12 years old with epilepsy who were prescribed daily antiseizure medication and were seen by the 13 providers in our Neurology QI Program. Using provider interviews, we undertook a root cause analysis of low counseling rates and identified the following main factors: insufficient time during clinic visit to counsel, lack of provider knowledge, and forgetting to counsel. Countermeasures were designed to address these main root causes and were implemented through iterative plan-do-study-act (PDSA) cycles. Interventions included provider education and features within the electronic health record, which were introduced sequentially, culminating in the creation of a best practice advisory (BPA). We performed biweekly chart reviews of visits for applicable patients to establish baseline performance rate and track progress over time. We used a statistical process control p-chart to analyze the outcome measure of documented counseling. As a balancing measure, clinicians were surveyed using the Technology Adoption Model survey to assess acceptance of the BPA. RESULTS: From September 2019 to August 2022, the QI team improved rates of documented folic acid counseling from 23% to 73% through several PDSA cycles. This level of performance has been sustained over time. The most successful and sustainable intervention was the BPA. Provider acceptance of the BPA was overall positive. SIGNIFICANCE: We successfully used QI methodology to improve and sustain our rates of documented folic acid supplementation counseling for adolescent females with epilepsy.

Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure prediction models: a retrospective cohort study.

BACKGROUND: Accurate prediction of seizures can help to direct resource-intense continuous electroencephalogram (CEEG) monitoring to neonates at high risk of seizures. We aimed to use data from standardised EEG reports to generate seizure prediction models for vulnerable neonates. METHODS: In this retrospective cohort study, we included neonates who underwent CEEG during the first 30 days of life at the Children's Hospital of Philadelphia (Philadelphia, PA, USA). The hypoxic ischaemic encephalopathy subgroup included only patients with CEEG data during the first 5 days of life, International Classification of Diseases, revision 10, codes for hypoxic ischaemic encephalopathy, and documented therapeutic hypothermia. In January, 2018, we implemented a novel CEEG reporting system within the electronic medical record (EMR) using common data elements that incorporated standardised terminology. All neonatal CEEG data from Jan 10, 2018, to Feb 15, 2022, were extracted from the EMR using age at the time of CEEG. We developed logistic regression, decision tree, and random forest models of neonatal seizure prediction using EEG features on day 1 to predict seizures on future days. FINDINGS: We evaluated 1117 neonates, including 150 neonates with hypoxic ischaemic encephalopathy, with CEEG data reported using standardised templates between Jan 10, 2018, and Feb 15, 2022. Implementation of a consistent EEG reporting system that documents discrete and standardised EEG variables resulted in more than 95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered on the basis of specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of neonates at high risk of seizures. Random forest models incorporating background features performed with classification accuracies of up to 90% (95% CI 83-94) for all neonates and 97% (88-99) for neonates with hypoxic ischaemic encephalopathy; recall (sensitivity) of up to 97% (91-100) for all neonates and 100% (100-100) for neonates with hypoxic ischaemic encephalopathy; and precision (positive predictive value) of up to 92% (84-96) in the overall cohort and 97% (80-99) in neonates with hypoxic ischaemic encephalopathy. INTERPRETATION: Using data extracted from the standardised EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of more than 90%. This information, incorporated into routine care, could guide decisions about the necessity of continuing EEG monitoring beyond the first day, thereby improving the allocation of limited CEEG resources. Additionally, this analysis shows the benefits of standardised clinical data collection, which can drive learning health system approaches to personalised CEEG use. FUNDING: Children's Hospital of Philadelphia, the Hartwell Foundation, the National Institute of Neurological Disorders and Stroke, and the Wolfson Foundation.

Assessing seizure burden in pediatric epilepsy using an electronic medical record-based tool through a common data element approach.

OBJECTIVE: Improvement in epilepsy care requires standardized methods to assess disease severity. We report the results of implementing common data elements (CDEs) to document epilepsy history data in the electronic medical record (EMR) after 12 months of clinical use in outpatient encounters. METHODS: Data regarding seizure frequency were collected during routine clinical encounters using a CDE-based form within our EMR. We extracted CDE data from the EMR and developed measurements for seizure severity and seizure improvement scores. Seizure burden and improvement was evaluated by patient demographic and encounter variables for in-person and telemedicine encounters. RESULTS: We assessed a total of 1696 encounters in 1038 individuals with childhood epilepsies between September 6, 2019 and September 11, 2020 contributed by 32 distinct providers. Childhood absence epilepsy (n = 121), Lennox-Gastaut syndrome (n = 86), and Dravet syndrome (n = 42) were the most common epilepsy syndromes. Overall, 43% (737/1696) of individuals had at least monthly seizures, 17% (296/1696) had a least daily seizures, and 18% (311/1696) were seizure-free for >12 months. Quantification of absolute seizure burden and changes in seizure burden over time differed between epilepsy syndromes, including high and persistent seizure burden in patients with Lennox-Gastaut syndrome. Individuals seen via telemedicine or in-person encounters had comparable seizure frequencies. Individuals identifying as Hispanic/Latino, particularly from postal codes with lower median household incomes, were more likely to have ongoing seizures that worsened over time. SIGNIFICANCE: Standardized documentation of clinical data in childhood epilepsies through CDE can be implemented in routine clinical care at scale and enables assessment of disease burden, including characterization of seizure burden over time. Our data provide insights into heterogeneous patterns of seizure control in common pediatric epilepsy syndromes and will inform future initiatives focusing on patient-centered outcomes in childhood epilepsies, including the impact of telemedicine and health care disparities.

Acceptability of Standardized EEG Reporting in an Electronic Health Record.

RATIONALE: Implementation of electronic health records may improve the quality, accuracy, timeliness, and availability of documentation. Thus, our institution developed a system that integrated EEG ordering, scheduling, standardized reporting, and billing. Given the importance of user perceptions for successful implementation, we performed a quality improvement study to evaluate electroencephalographer satisfaction with the new EEG report system. METHODS: We implemented an EEG report system that was integrated in an electronic health record. In this single-center quality improvement study, we surveyed electroencephalographers regarding overall acceptability, report standardization, workflow efficiency, documentation quality, and fellow education using a 0 to 5 scale (with 5 denoting best). RESULTS: Eighteen electroencephalographers responded to the survey. The median score for recommending the overall system to a colleague was 5 (range 3-5), which indicated good overall satisfaction and acceptance of the system. The median scores for report standardization (4; 3-5) and workflow efficiency (4.5; 3-5) indicated that respondents perceived the system as useful and easy to use for documentation tasks. The median scores for quality of documentation (4.5; 1-5) and fellow education (4; 1-5) indicated that although most respondents believed the system provided good quality reports and helped with fellow education, a small number of respondents had substantially different views (ratings of 1). CONCLUSIONS: Overall electroencephalographer satisfaction with the new EEG report system was high, as were the scores for perceived usefulness (assessed as standardization, documentation quality, and education) and ease of use (assessed as workflow efficiency). Future study is needed to determine whether implementation yields useful data for clinical research and quality improvement studies or improves EEG report standardization.