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Background We developed and implemented a structured clinical documentation support (SCDS) toolkit within the electronic medical record, to optimize patient care, facilitate documentation, and capture data at office visits in a sleep medicine/neurology clinic for patient care and research collaboration internally and with other centers. Methods To build our SCDS toolkit, physicians met frequently to develop content, define the cohort, select outcome measures, and delineate factors known to modify disease progression. We assigned tasks to the care team and mapped data elements to the progress note. Programmer analysts built and tested the SCDS toolkit, which included several score tests. Auto scored and interpreted tests included the Generalized Anxiety Disorder 7-item, Center for Epidemiological Studies Depression Scale, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Insomnia Severity Index, and the International Restless Legs Syndrome Study Group Rating Scale. The SCDS toolkits also provided clinical decision support (untreated anxiety or depression) and prompted enrollment of patients in a DNA biobank. Results The structured clinical documentation toolkit captures hundreds of fields of discrete data at each office visit. This data can be displayed in tables or graphical form. Best practice advisories within the toolkit alert physicians when a quality improvement opportunity exists. As of May 1, 2019, we have used the toolkit to evaluate 18,105 sleep patients at initial visit. We are also collecting longitudinal data on patients who return for annual visits using the standardized toolkits. We provide a description of our development process and screenshots of our toolkits. Conclusions The electronic medical record can be structured to standardize Sleep Medicine office visits, capture data, and support multicenter quality improvement and practice-based research initiatives for sleep patients at the point of care.

Objective: To use the electronic medical record (EMR) to optimize patient care, facilitate documentation, and support quality improvement and practice-based research in a concussion (mild traumatic brain injury; mTBI) clinic.Methods: We built a customized structured clinical documentation support (SCDS) toolkit for patients in a concussion specialty clinic. The toolkit collected hundreds of fields of discrete, standardized data. Autoscored and interpreted score tests include the Generalized Anxiety Disorder 7-item scale, Center for Epidemiology Studies Depression scale, Insomnia Severity Index, and Glasgow Coma Scale. Additionally, quantitative score measures are related to immediate memory, concentration, and delayed recall. All of this data collection occurred in a standard appointment length.Results: To date, we evaluated 619 patients at an initial office visit after an mTBI. We provided a description of our toolkit development process, and a summary of the data electronically captured using the toolkit.Conclusions: The electronic medical record can be used to effectively structure and standardize care in a concussion clinic. The toolkit supports the delivery of care consistent with Best Practices, provides opportunities for point of care decision support, and writes comprehensive progress notes that can be communicated to other providers.

Objective To use the electronic medical record (EMR) to optimize patient care, facilitate documentation, and support quality improvement and practice-based research, in a headache specialty clinic. Background Many physicians enter data into the EMR as unstructured free text and not as discrete data. This makes it challenging to use data for quality improvement or research initiatives. Methods We describe the process of building a customized structured clinical documentation support toolkit, specific for patients seen in a headache specialty clinic. The content was developed through frequent physician meetings to reach consensus on elements that define clinical Best Practices. Tasks were assigned to the care team and data mapped to the progress note. Results The toolkit collects hundreds of fields of discrete, standardized data. Auto scored and interpreted score tests include the Generalized Anxiety Disorder 7-item, Center for Epidemiology Studies Depression Scale, Migraine Disability Assessment questionnaire, Insomnia Sleep Index, and Migraine-Specific Quality of Life. We have developed Best Practice Advisories (BPA) and other clinical documentation support tools that alert physicians, when appropriate. As of April 1, 2018, we have used the toolkits at 4346 initial patient visits. We provide screenshots of our toolkits, details of data fields collected, and diagnoses of patients at the initial visit. Conclusions The EMR can be used to effectively structure and standardize headache clinic visits for quality improvement and practice-based research. We are sharing our proprietary toolkit with other clinics as part of the Neurology Practice-Based Research Network. These tools are also facilitating clinical research enrollment and a pragmatic trial of comparative effectiveness at the point-of-care among migraine patients.

ObjectivesTo demonstrate the feasibility of pragmatic clinical trials comparing the effectiveness of treatments using the electronic medical record (EMR) and an adaptive assignment design.MethodsWe 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).ResultsWe 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.DiscussionThis proof of concept article demonstrates the feasibility of conducting pragmatic trials using the EMR and an adaptive design.ConclusionThe 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.

Objective We describe our experience with routinely capturing and analyzing Mediterranean diet data via structured clinical documentation support tools built into the electronic medical record and describe adherence to the Mediterranean diet in patients at risk for either stroke or dementia in a US neurology clinical practice. Patients and Methods The Mediterranean diet is associated with a reduced risk of stroke and dementia. The Department of Neurology at NorthShore University HealthSystem routinely evaluates patients at initial and annual outpatient visits using structured clinical documentation support (SCDS) tools built into the electronic medical record (EMR). For patient evaluations in our Vascular Neurology and Brain Health subspecialty clinics, SCDS tools in the EMR include the validated 14-item questionnaire for Mediterranean diet adherence (PREvencion con DIeta MEDiterranea [PREDIMED]) that autoscores, auto-interprets, writes to the progress note, and electronically captures data. Our study population includes patients seen at these clinics from July 1, 2015, through November 29, 2017. Results At their initial office visit, 25.5% (95/373) of Brain Health patients scored 10 or more points ("strongly adherent") on the PREDIMED (median, 8; range, 0-14) whereas 6.7% (55/829) of Vascular Neurology patients achieved a score of 10 or more points (median, 6; range, 0-12). By contrast, 34.7% (2586/7447) of individuals in the original PREDIMED cohort were strongly adherent to the Mediterranean diet. Conclusion PREDIMED scores can be electronically captured to tailor nutrition interventions by assessing baseline adherence at the time of their initial neurology clinic visit. Patients in our Midwestern US clinics were weakly adherent to the Mediterranean diet. This suggests a major opportunity for nutrition intervention and education in US neurology clinical practices, toward preserving and improving brain health.

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 and Purpose: Standardized electronic medical record tools provide an opportunity to efficiently provide care that conforms to Best Practices and supports quality improvement and practice-based research initiatives. Methods: We describe the development of a customized structured clinical documentation “toolkit” that standardizes patient data collection to conform to Best Practices for treating patients with stroke. The toolkit collects patients’ demographic information, relevant score test measures, and captures information on disability, treatment, and outcomes. Results: We describe here our creation and implementation of the toolkits and provide example screenshots. As of August 1, 2018, we have evaluated 2332 patients at an initial visit for a possible stroke. We provide basic descriptive data gathered from the use of the toolkits, demonstrating their utility in collecting patient data in a manner that supports both quality clinical care and research initiatives. Conclusions: We have developed an EMR toolkit to support Best Practices in the care of patients with stroke. We discuss 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.

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.