(This is the latest in a series of blog posts looking at AI’s potential impact on chronic care management in healthcare.)

According to the National Institute of Neurological Disorders and Stroke, Low Back Pain (LBP) is a common disorder involving the muscles, nerves and bones of the back. Worldwide, LBP is the single greatest cause of disability and approx. 2% of the United States population is disabled from chronic LBP.

With such a significant size of the global population affected by LBP, it’s not surprising that tech vendors are working with doctors and specialists to investigate how Artificial Intelligence (AI) can be used to assist patients and providers in LBP treatment regimens. As we continue our series of blogs looking at AI’s potential role in chronic care management, we’ll review in this post some promising AI use cases for LBP, including ones involving our industry-leading digital colleague, Amelia.

LBP’s Far-Reaching Impact

As patients with LBP can attest, not all back pain is alike, and not all pain lasts the same amount of time. LBP can vary from a dull constant ache to a sudden sharp feeling, and may be classified by duration as acute (pain lasting less than four weeks), sub-acute (four to 12 weeks), or chronic (more than 12 weeks). It’s not only patients’ overall health and well-being impacted by this condition. The economic consequences of LBP within the US are sizable, accounting for approx. 15% of annual healthcare provider visits.  The cumulative costs associated with provider visits, decreased employee wages due to sick leave and lost productivity total approx. $200 billion.

Risk factors for LBP are divided into two broad categories; non-modifiable (age, sex, family history, ethnicity, etc.) and modifiable (smoking, depressive disorders, infrequent physical activity, high body mass index [BMI], etc.).

The most relevant contributors to LBP, aside from its risk factors, are single physical events such as twisting, bending or lifting, repetitive exposures to mechanical stress and age-related degenerative spinal changes. In the case of chronic LBP, psychosocial factors are a primary contributory factor, while mechanical and bio-physiologic factors are secondary.

Interestingly, care-related delivery and patient-provider communication styles are being recognized as additional risk factors, exacerbating patients’ LBP or influencing its chronicity. This may be due in part to the fact that approx. 85% of LBP lacks readily identifiable causes and subsequently it is often termed as nonspecific.

Unfortunately, the public has a generalized belief that LBP must be caused by a demonstrable structural cause within the back itself, versus surrounding muscles and nerves or another system, resulting in excessive demands upon providers for a wide range of diagnostic testing. This results in higher overall direct and indirect costs for patients and providers, not to mention wasted time on tests and procedures that ultimately may not provide any patient relief.

Treatment Plans Growing in Complexity

For many patients, LBP often spreads beyond a single point of back discomfort, resulting in associated pains and conditions. LBP usually expands past the original peripheral spinal point or origin to involve a broader network of peripheral and central nervous system pathways and structures.  This creates a complex treatment paradigm involving multi-modality therapies, including a wide range of medications, education recommendations, self-managed care and other treatments.  Medication management is particularly difficult as a variety of drugs are prescribed to treat chronic LBP.

Fortunately, LBP in all of its manifestations, especially chronic conditions, are readily addressable through AI platforms with cognitive digital agents such as Amelia. Given the extent of LBP conditions and treatments, not to mention the widespread nature of the condition, an AI platform’s ability to exploit Machine Learning for scale makes it ideally suited to address LBP treatment.

Applying AI to Help Patients and Caregivers

Amelia’s attributes, in particular, including her ability to integrate with systems of record (electronic medical records or EMRs, PBX phone systems, ERP for finance and billing etc.), a robust Natural Language Interface (NLI) as a conversational front end (via text, voice and other channels), and her built-in analytical abilities enable her to play several meaningful roles in LBP treatment regimens. Although Amelia can be trained to take on a variety of duties, her ability to assume two roles in particular can provide substantial near-term benefits for LBP patients and providers by addressing high-volume needs:

Patient scheduling: Patients with chronic LBP engage in complex scheduling of appointments, treatments and tests across multiple provider locations, as well as with ancillary centers related to medical imaging and physical therapy. This also involves adding, cancelling or changing appointments as necessary. Currently most patient appointment systems are phone-based or conducted through provider patient portals or similar channels, which can be confusing or time-consuming to navigate. With Amelia’s ability to integrate into hospital and call center systems, patients and providers could interact directly with Amelia’s interface for appointment and testing scheduling, freeing up time for provider and hospital staff to focus on patient and clinical needs. Patients, meanwhile, can make appointment changes and modifications through a conversation or text chat with Amelia, eliminating the need to make multiple phone calls or log into a patient portal.

Provider and caregiver assistance: New LBP patients are initially assessed and categorized, and each case requires a careful clinical assessment for LBP signs and symptoms and the associated pathology. Correct diagnosis is vital if an LBP patient is to receive the appropriate level of care and the correct treatment regimen, with potentially multiple therapies and medications prescribed. Amelia, with her ability to read and understand large data sets, can be an important source of information for doctors and nurse practitioners during LBP diagnosis, ensuring that caregivers are armed with the most up-to-date clinical practice guidelines. Amelia is especially well-suited to assist visiting nurses within patients’ homes as they perform physical examinations designed to identify subsets of patients who may have pathological conditions requiring urgent intervention.

Implementation of a conversational AI platform such as Amelia can help accelerate LBP diagnosis and treatment with her ability to integrate, scale and connect with patients and providers, with additional implications on cost, productivity and care efficiency on the overall LBP trend in the longer term.

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