We describe quality improvement and practice-based research using the electronic medical record (EMR) in a community health system–based department of neurology. Our care transformation initiative targets 10 neurologic disorders (brain tumors, epilepsy, migraine, memory disorders, mild traumatic brain injury, multiple sclerosis, neuropathy, Parkinson disease, restless legs syndrome, and stroke) and brain health (risk assessments and interventions to prevent Alzheimer disease and related disorders in targeted populations). Our informatics methods include building and implementing structured clinical documentation support tools in the EMR; electronic data capture; enrollment, data quality, and descriptive reports; quality improvement projects; clinical decision support tools; subgroup-based adaptive assignments and pragmatic trials; and DNA biobanking. We are sharing EMR tools and deidentified data with other departments toward the creation of a Neurology Practice-Based Research Network. We discuss practical points to assist other clinical practices to make quality improvements and practice-based research in neurology using the EMR a reality.

Objectives: To demonstrate the feasibility of pragmatic clinical trials comparing the effectiveness of treatments using the electronic medical record (EMR) and an adaptive assignment design.Methods: We have designed and are implementing pragmatic trials at the point-of-care using custom-designed structured clinical documentation support and clinical decision support tools within our physician’s typical EMR workflow. We are applying a subgroup based adaptive design (SUBA) that enriches treatment assignments based on baseline characteristics and prior outcomes. SUBA uses information from a randomization phase (phase 1, equal randomization, 120 patients), to adaptively assign treatments to the remaining participants (at least 300 additional patients total) based on a Bayesian hierarchical model. Enrollment in phase 1 is underway in our neurology clinical practices for 2 separate trials using this method, for migraine and mild cognitive impairment (MCI).Results: We are successfully collecting structured data, in the context of the providers’ clinical workflow, necessary to conduct our trials. We are currently enrolling patients in 2 point-of-care trials of non-inferior treatments. As of March 1, 2018, we have enrolled 36% of eligible patients into our migraine study and 63% of eligible patients into our MCI study. Enrollment is ongoing and validation of outcomes has begun.Discussion: This proof of concept article demonstrates the feasibility of conducting pragmatic trials using the EMR and an adaptive design.Conclusion: The demonstration of successful pragmatic clinical trials based on a customized EMR and adaptive design is an important next step in achieving personalized medicine and provides a framework for future studies of comparative effectiveness.

Background: Many physicians enter data into the electronic medical record (EMR) as unstructured free text and not as discrete data, making it challenging to use for quality improvement or research initiatives. Objectives: The objective of this research paper was to develop and implement a structured clinical documentation support (SCDS) toolkit within the EMR to facilitate quality initiatives and practice-based research in a multiple sclerosis (MS) practice. Methods: We built customized EMR toolkits to capture standardized data at office visits. Content was determined through physician consensus on necessary elements to support best practices in treating patients with demyelinating disorders. We also developed CDS tools and best practice advisories within the toolkits to alert physicians when a quality improvement opportunity exists, including enrollment into our DNA biobanking study at the point of care. Results: We have used the toolkit to evaluate 541 MS patients in our clinic and begun collecting longitudinal data on patients who return for annual visits. We provide a description and example screenshots of our toolkits, and a brief description of our cohort to date. Conclusions: The EMR can be effectively structured to standardize MS clinic office visits, capture data, and support quality improvement and practice-based research initiatives at the point of care.

The electronic medical record (EMR) presents an opportunity to standardize patient data collection based on quality guidelines and conduct practice-based research. We describe the development of a customized EMR “toolkit” that standardizes patient data collection with hundreds of discrete fields that supports Best Practices for treating patients with memory disorders. The toolkit also supports practice-based research. We describe the design and successful implementation of a customized EMR toolkit to support Best Practices in the care of patients with memory disorders. We discuss applications, including quality improvement projects and current research initiatives, using the toolkit. This toolkit is being shared with other departments of Neurology as part of the Neurology Practice-Based Research Network. Data collection is ongoing, including longitudinal follow-up. This toolkit will generate data that will allow for descriptive and hypothesis driven research as well-quality improvement among patients seen in a memory clinic.

Background/Objective: Genome-wide association studies (GWASs) demonstrate a complex genetic landscape for migraine risk. Migraine polygenic risk scores (PRSs) developed from GWAS data may have utility for predicting disease course. We analyzed the strength of association between an integrative migraine PRS and age at onset and chronification. Methods: In this retrospective clinical/genetic case–control study, PGS004799 was calculated for adults with European ancestry from two real-world community cohorts. In the DodoNA cohort, 1653 treated, deeply phenotyped migraine cases, diagnosed using International Classification of Headache Disorders 3rd edition criteria, were followed for a mean (range) of 2.3 (1–10) years and compared to 3460 controls (without migraine diagnosis). In the GHI cohort, 2443 cases were identified using the first migraine ICD code as a proxy for migraine onset and compared to 8576 controls (without migraine ICD codes). PRS associations with age at onset (DodoNA) or first migraine ICD code (GHI) and chronification (DodoNA) were evaluated. Results: In both cohorts, PRS was higher in cases (DodoNA mean (range) cases: 0.82 (0.07–1.76), controls: 0.78 (0.04–1.56); t (5111) = −6.1, p = 1.4 × 10⁻⁹, GHI: cases: 0.79 (0.003–1.68), controls: 0.75 (−0.06–1.53); t (11,017) = −7.69, p = 1.6 × 10⁻¹⁴), and a higher PRS was associated with earlier onset in females (HR [95% CI] DodoNA: 2.1 [1.6–2.6, p < 0.001; GHI: 1.8 [1.4–2.1], p < 0.001) and in males (DodoNA: 2.5 [1.3–4.7], p = 0.005; GHI: 1.6 [1.1–2.6], p = 0.027). PRS was not different in cases with or without chronification (t (1651) = −1.67, p = 0.094) and was not associated with earlier chronification (1.2 [0.8–1.6], p = 0.424). Conclusions: Higher genetic risk was associated with earlier onset and increased risk of migraine well into adulthood, but not with chronification. This suggests that the PRS quantifies genetic susceptibility that is distinct from factors influencing disease course.