Literature DB >> 34730240

Harnessing the power of the electronic health record for ALS research and quality improvement: CReATe CAPTURE-ALS and the ALS Toolkit.

Volkan Granit1, Anne-Laure Grignon1, Joanne Wuu1, Jonathan Katz2, David Walk3, Sumaira Hussain1, Jessica Hernandez1, Carlayne Jackson4, James Caress5, Tom Yosick6, Nancy Smider6, Michael Benatar1.   

Abstract

The electronic health record (EHR) is designed principally to support the provision and documentation of clinical care, as well as billing and insurance claims. Broad implementation of the EHR, however, also yields an opportunity to use EHR data for other purposes, including research and quality improvement. Indeed, effective use of clinical data for research purposes has been a long-standing goal of physicians who provide care for patients with ALS, but the quality and completeness of clinical data, as well as the burden of double data entry into the EHR and into a research database, have been persistent barriers. These factors provided motivation for the development of the ALS Toolkit, a set of interactive digital forms within the EHR that enable easy, consistent, and structured capture of information relevant to ALS patient care (as well as research and quality improvement) during clinical encounters. Routine use of the ALS Toolkit within the context of the CReATe Consortium's institutional review board-approved Clinical Procedures to Support Research in ALS (CAPTURE-ALS) study protocol, permits aggregation of structured ALS patient data, with the goals of empowering research and driving quality improvement. Widespread use of the ALS Toolkit through the CAPTURE-ALS protocol will help to ensure that ALS clinics become a driving force for collecting and aggregating clinical data in a way that reflects the true diversity of the populations affected by this disease, rather than the restricted subset of patients that currently participate in dedicated research studies.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  amyotrophic lateral sclerosis; electronic medical record; guidelines; motor neuron disease; quality improvement

Mesh:

Year:  2021        PMID: 34730240      PMCID: PMC8752483          DOI: 10.1002/mus.27454

Source DB:  PubMed          Journal:  Muscle Nerve        ISSN: 0148-639X            Impact factor:   3.217


  12 in total

1.  Accuracy and Completeness of Clinical Coding Using ICD-10 for Ambulatory Visits.

Authors:  Jan Horsky; Elizabeth A Drucker; Harley Z Ramelson
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

Review 2.  Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?

Authors:  Hiroshi Mitsumoto; Benjamin R Brooks; Vincenzo Silani
Journal:  Lancet Neurol       Date:  2014-11       Impact factor: 44.182

3.  The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III).

Authors:  J M Cedarbaum; N Stambler; E Malta; C Fuller; D Hilt; B Thurmond; A Nakanishi
Journal:  J Neurol Sci       Date:  1999-10-31       Impact factor: 3.181

Review 4.  Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.

Authors:  Joshua C Denny; Lisa Bastarache; Dan M Roden
Journal:  Annu Rev Genomics Hum Genet       Date:  2016-05-04       Impact factor: 8.929

5.  Electronic medical records for genetic research: results of the eMERGE consortium.

Authors:  Abel N Kho; Jennifer A Pacheco; Peggy L Peissig; Luke Rasmussen; Katherine M Newton; Noah Weston; Paul K Crane; Jyotishman Pathak; Christopher G Chute; Suzette J Bielinski; Iftikhar J Kullo; Rongling Li; Teri A Manolio; Rex L Chisholm; Joshua C Denny
Journal:  Sci Transl Med       Date:  2011-04-20       Impact factor: 17.956

6.  Scrutinizing enrollment in ALS clinical trials: room for improvement?

Authors:  Richard S Bedlack; Daniel M Pastula; Emily Welsh; Darlene Pulley; Merit E Cudkowicz
Journal:  Amyotroph Lateral Scler       Date:  2008-10

Review 7.  Quality improvement in neurology: amyotrophic lateral sclerosis quality measures: report of the quality measurement and reporting subcommittee of the American Academy of Neurology.

Authors:  Robert G Miller; Benjamin Rix Brooks; Rebecca J Swain-Eng; Robert C Basner; Gregory T Carter; Patricia Casey; Adam B Cohen; Richard Dubinsky; Dallas Forshew; Carlayne E Jackson; Ed Kasarskis; Nicholas J Procaccini; Mohammed Sanjak; Fredrik P Tolin
Journal:  Neurology       Date:  2013-11-22       Impact factor: 9.910

8.  MOVR-NeuroMuscular ObserVational Research, a unified data hub for neuromuscular diseases.

Authors:  R Rodney Howell; Stephan Zuchner
Journal:  Genet Med       Date:  2018-06-22       Impact factor: 8.822

Review 9.  Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.

Authors:  Barbara M Decker; Chloé E Hill; Steven N Baldassano; Pouya Khankhanian
Journal:  Seizure       Date:  2021-01-13       Impact factor: 3.184

10.  Use of EHRs data for clinical research: Historical progress and current applications.

Authors:  Amy Harris Nordo; Hugh P Levaux; Lauren B Becnel; Jose Galvez; Prasanna Rao; Komathi Stem; Era Prakash; Rebecca Daniels Kush
Journal:  Learn Health Syst       Date:  2019-01-16
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