scholarly journals Quality improvement and practice-based research in sleep medicine using structured clinical documentation in the electronic medical record

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Demetrius M. Maraganore ◽  
Thomas Freedom ◽  
Kelly Claire Simon ◽  
Lori E. Lovitz ◽  
Camelia Musleh ◽  
...  

Abstract 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.

Epilepsia ◽  
2016 ◽  
Vol 58 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Jaishree Narayanan ◽  
Sofia Dobrin ◽  
Janet Choi ◽  
Susan Rubin ◽  
Anna Pham ◽  
...  

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi158-vi159
Author(s):  
Ryan Merrell ◽  
Nina Martinez ◽  
Susan Stasinos ◽  
James Muresan ◽  
Shaun Walters ◽  
...  

2015 ◽  
Vol 5 (5) ◽  
pp. 419-429 ◽  
Author(s):  
Demetrius M. Maraganore ◽  
Roberta Frigerio ◽  
Nazia Kazmi ◽  
Steven L. Meyers ◽  
Meredith Sefa ◽  
...  

Brain Injury ◽  
2019 ◽  
Vol 34 (1) ◽  
pp. 62-67
Author(s):  
Kelly Claire Simon ◽  
Nicole Reams ◽  
Erik Beltran ◽  
Charles Wang ◽  
Bryce Hadsell ◽  
...  

2004 ◽  
Vol 11 (5) ◽  
pp. 351-357 ◽  
Author(s):  
Richard R. Owen ◽  
Carol R. Thrush ◽  
Dale Cannon ◽  
Kevin L. Sloan ◽  
Geoff Curran ◽  
...  

2018 ◽  
Vol 4 (4) ◽  
pp. 205521731881373 ◽  
Author(s):  
Kelly Claire Simon ◽  
Afif Hentati ◽  
Susan Rubin ◽  
Tiffani Franada ◽  
Darryck Maurer ◽  
...  

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.


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