Abstract
The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.
Highlights
At the time this article was published, there were over 33 million confirmed COVID-19 patients globally, with 1 million deaths being reported (Johns Hopkins University, 2020) in over 188 countries and territories
We propose the use of Natural Language Processing (NLP) and machine learning (ML) technologies to assist with analyzing the information contained in these clinical notes
The call notes consist of unstructured data that can be classified into three categories: History including previous patient diagnoses, medications, and existing symptoms; Action taken by the Rehabilitation Advice Line (RAL) advisor during the call including discussion of current symptoms, subjective over-the-phone assessment, and cause of the condition; Disposition detailing the advice provided or service referrals given to the patient
Summary
At the time this article was published, there were over 33 million confirmed COVID-19 patients globally, with 1 million deaths being reported (Johns Hopkins University, 2020) in over 188 countries and territories. This change in healthcare policies and priorities caused the treatment of non-emergent (chronic or non-life-threatening) conditions to be deferred into the future While this shift has allowed for focusing healthcare resources to address the immediate needs of the pandemic, healthcare systems had to delay and defer non-emergent treatments to mitigate or reduce the risk of COVID-19 infection to vulnerable populations in healthcare settings. The COVID-19 pandemic forced healthcare providers and healthcare systems worldwide to reduce or limit less-urgent healthcare services, such as rehabilitation services for people with acute and chronic diseases and disorders (Prvu Bettger et al, 2020) For some patients, this delay in treatment is inconvenient but not substantially detrimental.
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