Objective:

To integrate a clinical algorithm applied to neurologists’ electronic medical record (EMR) data to reduce the delay in time to diagnosis and treatment for patients with amyotrophic lateral sclerosis (ALS).

Background:

ALS is a rare and progressive neurodegenerative disease that is difficult to diagnose. An estimated 3000 Canadians are living with ALS and approximately 1000 are diagnosed annually. Delayed ALS diagnosis, reported to range from 9.1 to 27 months, can lead to mismanagement and deterioration of patient outcomes. To date, no single test definitively confirms ALS. Algorithm-based tools may be used to help physicians improve the timeliness and accuracy of ALS diagnosis.

Design/Methods:

A clinical algorithm applied to neurologists’ EMR data categorizes patients into risk groupings based on evidence of upper and lower motor neuron abnormalities and spinal region involvement. EMR records of participating clinics are scanned and the likelihood of ALS is estimated for patients whose records include a recent electromyography test. A report is sent to the clinic for each patient flagged for follow up. In an initial study, the algorithm demonstrated improved sensitivity and specificity of 93.9% and 98.0%, respectively, when compared to an original proof-of-concept study. This software has been registered as a class 1 medical device (Health Canada). An ongoing, 12-month pilot study aims to deploy this algorithm within 20 community clinics.

Results:

Results to be presented.

Conclusions:

Clinical algorithms may aid in the diagnosis of ALS when applied to real-world EMR data in neurological community practice. The algorithm in this study has been proven to identify patients with an elevated risk of ALS to support expedited follow up, diagnosis, and treatment. This study is ongoing, and additional details aimed to improve the results of earlier algorithm testing and increase exposure to the clinical community are currently being investigated.