ALS EMR Program Data

1.Applying a clinical algorithm on real world EMR data

A previous version of the algorithm was applied by Ensho Health to the EMR of Dr. Amer Ghavanini’s neurology practice in Ontario. The following poster was created to present the results at the Motor Neuron Disease Association (MNDA) and the Northeast ALS Consortium (NEALS) annual meetings.

(Click image for PDF version)

Patients were categorized into four risk groupings based on evidence of upper motor neuron (UMN) degeneration, lower motor neuron (LMN) degeneration and spinal region involvement.

3,372 patient records were analyzed
1,332 patients (39%) had evidence of UMN degeneration, LMN degeneration and/or spinal region involvement

  • 160 of these patients were categorized as having a very high risk for ALS
  • The alrorithm correctly identified the three patients with a prior diagnosis of ALS
  • One patient was determined to have have a high probability of ALS (and later confirmed to have ALS)

Results of Application of the Algorithm

Results of the clinical review for the 160 patients identified as having a very high risk


Ensho Health is a Canadian digital healthcare services company that provides on-demand analysis of health information to match patients with clinical trials, build better real-world evidence and accelerate rare disease diagnosis and treatment.

This initiative is supported by Mitsubishi Tanabe Pharma.

EOCI is the organizing committee and medical education partner for this initiative.