Aligning Clinical Intensity with Patient Needs: A New Approach in Digital Health

Traditional healthcare clinical workflows often rely on DRG (Diagnosis-Related Group) diagnosis and procedure codes to trigger healthcare actions. However, this approach presents significant challenges in automating guidance and recommending next steps that suit the healthcare consumer’s needs. Introducing the concept of acuity is crucial for directing individuals to interventions with the appropriate intensity.

Historically, most attempts to provide automated guidance to patients rely on diagnosis or procedure codes to predict patient needs. Although this can be helpful, codes are a blunt instrument that do not capture important pieces of information from patients about their current health and wellbeing.

Consider an automated intervention to assist a patient in getting musculoskeletal care. The patient carries a diagnosis code of M54.50 – Low Back Pain, Unspecified. Does this patient need referral to a second opinion service, physical therapy, or just general exercise? It is impossible to know from just a simple code.

Our Chief Medical Officer often remarks that physicians don’t reflexively order an MRI for everyone with back pain. Yet, in digital health, we often adopt a one-size-fits-all approach, opting for expensive solutions for every issue because there haven’t been good ways to match patients to a better fit solution.

At Solera, our acuity matching approach uses patient-reported symptoms, functional impairment and other markers to determine the best starting point across a network of solutions of various levels of intensity. In the example of low back pain above, a member with intermittent, low level discomfort may be most appropriate to start with general exercise, while a member with chronic pain will be best served through immediate referral to physical therapy. By having a network of solutions with varying clinical intensities, we can match consumers to interventions that fit their needs. . Aligning intervention intensity with consumer needs helps maintain an optimal cost structure, as lower intensity often means lower cost.

To develop and implement an acuity model effectively, it’s essential to:

1. Focus on the Needs of the Patient/Consumer: Rather than just addressing the condition, ask questions that identify the clinical severity of the symptoms to align the intervention’s intensity with what’s needed.
2. Offer a Broad Range of Solutions: Digital solutions should span from self-paced preventative services to options that include medical interventions. Offering solutions on a spectrum enables matching clinical intensity to patient need – just like in brick-and-mortar care where not every patient needs a specialist or advanced therapies.
3. Track Outcomes According to Acuity Levels: A model is only as good as the data it’s trained on. Ensure the outcomes tracked are suitable for the condition and acuity level. Critically assess whether the solution meets the consumer’s needs.

Large language models will make assessing an individual’s acuity more scalable. However, the need for multiple intervention intensities will persist.

Remember, not everyone with back pain needs an MRI. This principle is vital for maximizing the effectiveness of digital health solutions.

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