Article Type
Changed
Fri, 11/01/2019 - 14:33
Display Headline
Algorithms help identify RRMS patients

Key clinical point: Two algorithms identified in a new study can be used for future clinical research of relapsing-remitting multiple sclerosis (RRMS). Major finding: Using EHRs and the coded health care claims of 5,308 patients with possible MS, 837 and 2,271 were identified as having RRMS, respectively. There were also 779 patients identified using both algorithms.

Study details: Two different algorithms “unstructured clinical notes (EHR clinical notes-based algorithm) and structured/coded data (claims-based algorithms)” were used to identify patients with RRMS.

Disclosures: The investigators reported no conflicts of interest.

Citation: Van Le H, et al. Value Health. 2019 Jan;22(1):77-84. doi: 10.1016/j.jval.2018.06.014.

Publications
Topics
Sections

Key clinical point: Two algorithms identified in a new study can be used for future clinical research of relapsing-remitting multiple sclerosis (RRMS). Major finding: Using EHRs and the coded health care claims of 5,308 patients with possible MS, 837 and 2,271 were identified as having RRMS, respectively. There were also 779 patients identified using both algorithms.

Study details: Two different algorithms “unstructured clinical notes (EHR clinical notes-based algorithm) and structured/coded data (claims-based algorithms)” were used to identify patients with RRMS.

Disclosures: The investigators reported no conflicts of interest.

Citation: Van Le H, et al. Value Health. 2019 Jan;22(1):77-84. doi: 10.1016/j.jval.2018.06.014.

Key clinical point: Two algorithms identified in a new study can be used for future clinical research of relapsing-remitting multiple sclerosis (RRMS). Major finding: Using EHRs and the coded health care claims of 5,308 patients with possible MS, 837 and 2,271 were identified as having RRMS, respectively. There were also 779 patients identified using both algorithms.

Study details: Two different algorithms “unstructured clinical notes (EHR clinical notes-based algorithm) and structured/coded data (claims-based algorithms)” were used to identify patients with RRMS.

Disclosures: The investigators reported no conflicts of interest.

Citation: Van Le H, et al. Value Health. 2019 Jan;22(1):77-84. doi: 10.1016/j.jval.2018.06.014.

Publications
Publications
Topics
Article Type
Display Headline
Algorithms help identify RRMS patients
Display Headline
Algorithms help identify RRMS patients
Sections
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Disqus Comments
Default
Gate On Date
Fri, 11/01/2019 - 14:30
Un-Gate On Date
Fri, 11/01/2019 - 14:30
Use ProPublica
CFC Schedule Remove Status
Fri, 11/01/2019 - 14:30
Hide sidebar & use full width
render the right sidebar.