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Sleep science and practice 2020 Jan 02
Authors:
Maraganore DM
Abstract
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.
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.
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.
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.
PMID:
32395635
[PubMed - as supplied by publisher]
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Authors:
Simon KC, Tideman S, Hillman L, Lai R, Jathar R, Ji Y, Bergman-Bock S, Castle J, Franada T, Freedom T, Marcus R, Mark A, Meyers S, Rubin S, Semenov I, Yucus C, Pham A, Garduno L, Szela M, Frigerio R, Maraganore DM
Abstract
To demonstrate the feasibility of pragmatic clinical trials comparing the effectiveness of treatments using the electronic medical record (EMR) and an adaptive assignment design.
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).
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.
This proof of concept article demonstrates the feasibility of conducting pragmatic trials using the EMR and an adaptive design.
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.
PMID:
30386852
[PubMed - as supplied by publisher]
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Authors:
Meyers S, Claire Simon K, Bergman-Bock S, Campanella F, Marcus R, Mark A, Freedom T, Rubin S, Semenov I, Lai R, Hillman L, Tideman S, Pham A, Frigerio R, Maraganore DM
Abstract
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.
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.
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.
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.
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.
PMID:
30066412
[PubMed - as supplied by publisher]
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Disease-a-month : DM 2015 Jun
Authors:
Freedom T
Abstract
Headaches and sleep disorders are associated in a complex manner. Both the disorders are common in the general population, but the relationship between the two is more than coincidental. Sleep disorders can exacerbate headache sand the converse is also true. Treatment of sleep disorders can have a positive impact on the treatment of headaches. Screening for sleep disorders should be considered in all patients with headaches. This can be accomplished with brief screening tools. Those who screen positively can be further evaluated or referred to asleep specialist.
PMID:
25951784
[PubMed - as supplied by publisher]
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Disease-a-month : DM 2015 Jun
Authors:
Freedom T
PMID:
25913497
[PubMed - as supplied by publisher]
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Authors:
Freedom T
PMID:
24400765
[PubMed - as supplied by publisher]
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Authors:
Freedom T, Evans RW
Abstract
Many patients with headache disorders have coexisting sleep difficulties. As both conditions are relatively common, they could potentially be present simultaneously, even if unrelated. However, there is evidence that a comorbid association between headache and sleep disorders exists.
PMID:
23848227
[PubMed - as supplied by publisher]
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Sleep & breathing = Schlaf & Atmung 2013 Dec
Authors:
Kuźniar TJ, Kasibowska-Kuźniar K, Ray DW, Freedom T
Abstract
The definition of complex sleep apnea (CompSAS) encompasses patients with obstructive sleep apnea (OSA) who develop central apnea activity upon restitution of airway patency. Presence of arterial hypertension (HTN), coronary artery disease (CAD) and heart failure (HF) have been proposed as risk factors for CompSAS among OSA patients. Using our database of patients with CompSAS, we examined the prevalence of these risk factors and defined other clinical characteristics of patients with CompSAS.
Through retrospective search of the database, we examined the medical and clinical characteristics of consecutive patients diagnosed with CompSAS between 11/1/2006 and 6/30/2011 at NorthShore University HealthSystem.
One hundred and fifty patients with CompSAS were identified. Among patients included in the study, 97 (64.7 %) had at least one risk factor for CompSAS, while 53 (35.3 %) did not have any of them. Prevalence of low left ventricular ejection fraction and hypocapnia were low. Therapeutic interventions consisted of several positive airway pressure therapies, mainly adaptive servo ventilation. A hundred and ten patients (73.3 %) complied with recommended therapy and improved clinically.
Although most patients with CompSAS have cardiac comorbidities, about one third of patients do not have any risk factors of CompSAS prior to sleep testing. Further research on factors involved in development of CompSAS will allow for better tailoring of therapy to pathophysiology involved in an individual case.
PMID:
23436008
[PubMed - as supplied by publisher]
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Pneumonologia i alergologia polska 2012
Authors:
Kuźniar TJ, Kasibowska-Kuźniar K, Freedom T
Abstract
Patients with complex sleep apnoea (CompSAS) have obstructive sleep apnoea and experience persistent central apnoeas when exposed to positive airway pressure. Elevated loop gain is one of the postulated mechanisms of CompSAS. We speculated that bilevel positive airway pressure - spontaneous (BPAP-S), by producing relative hyperventilation, may more readily produce CompSAS activity than continuous positive airway pressure (CPAP). If found to do so, a trial of BPAP-S might be a simple way of identifying patients with elevated loop gain who are at risk for CompSAS.
Thirty-nine patients with complex sleep apnoea were included in the study. Segments of NREM sleep on CPAP and BPAP-S matched for body position and expiratory airway pressure (comparison pressure) were retrospectively analysed. Correlations between clinical and demographic variables and polysomnographic response to CPAP and BPAP-S were sought.
There was no difference in any of the polysomnographic indices on CPAP and BPAP-S. In 19 patients the use of CPAP was associated with lower AHI at the comparison pressure; in 20 patients the opposite was true. No clinical variables correlated to the differential response to CPAP vs. BPAP-S.
BPAP-S was not more effective than CPAP in stimulating complex sleep apnoea activity.
PMID:
22562273
[PubMed - as supplied by publisher]
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Disease-a-month : DM 2011 Aug
Authors:
Freedom T
PMID:
21929967
[PubMed - as supplied by publisher]