Healthcare AI Company and Neurological Center Collaborating on New Tool to Improve ALS Management

CloudMedx plans to develop a computer analysis algorithm that will track the clinical progression of patients with amyotrophic lateral sclerosis (ALS) to predict treatment outcomes.

This tool will be the result of a new collaboration between the healthcare artificial intelligence (AI) company and the Gregory W. Fulton ALS and Neuromuscular Disease Center at Barrow Neurological Institute in Phoenix.

CloudMedx is working closely with Shafeeq Ladha, MD, a neurologist who specializes in ALS treatment at the center, and his team to build this tool.

The new tool is expected to help identify better predictors of disease progression and patient survival, which may lead to faster development of treatments. It may also increase the possibility of patients receiving personalized treatments, with the potential to improve outcomes and help with disease management.

“We are excited to embark on this journey with The Barrow,” Tashfeen Suleman, CEO of CloudMedx, said in a press release. “Using AI assistive tools that may become part of the clinician’s workflows may give them augmentative tools that help in the critical decision-making process, especially for near fatal diseases like ALS.”

“This collaboration has the potential to improve delivery of care for patients suffering from ALS and their family members. Our hope is that with the tools available to us, we can find ways to improve this process and bring about a positive change in a collaborative manner with The Barrow,” he said.

Most currently available strategies for clinicians to help patients manage their health do not take into consideration a patient’s particular condition and overall health history, such as the presence of secondary disorders or risk factors.

More than 80 percent of data stored in electronic health records and clinical workflows gets overlooked by researchers. This is mainly due to a lack of time and resources to analyze so much data, which often may be “unstructured” in the form of notes, discharge summaries, and unused test results.

To overcome these limitations, the team will include data collected from patients’ electronic health records, lab and radiology results, as well as other sources, to build an analysis algorithm to predict patient risks, treatment plans, and outcomes.

They will take advantage of CloudMedx’s AI platform to improve knowledge on the disease, using these patient records, which they expect will provide clues on appropriate strategies to achieve the best outcome. The algorithm will read the data and condense it into useful information the physician can use to help his ALS patient manage the disease.

“As a heterogenous disease that varies greatly from patient to patient, ALS poses numerous challenges to clinicians seeking better treatments,” Ladha said. “Our goal in working with CloudMedx is to see if we can identify clinical features that will help us identify patients earlier and improve our ability to predict the course of their disease. If we can accomplish this goal, it will translate to better care and more rapid treatment development.”