How Can Healthcare Companies Identify Who Needs Remediation Programs?

What Is Remediation?

The Cambridge Dictionary defines remediation as “the process of improving or correcting a situation.”  Remediation programs are commonly employed in teaching and education wherein they address learning gaps by reteaching basic skills with a focus on core areas like reading and math.  And as pointed out in an understood.org article, remedial programs are expanding in many places in our post-COVID 19 world.

In healthcare, there’s a wide range of remediation programs, or “remedial care,” diversified based on their end goal which may include smoking cessation, anti-obesity, weight reduction, diet improvement, exercise, heart-healthy living, alcoholism treatment, drug treatment, and more.  But how do you identify the people who need remedial care the most?

Who Needs Remediation?

You might say you can tell who needs remedial care by just looking at the physical aspect of the prospective patient, but this is a shortsighted answer to the question.  And what about those who need remedial care for a heart-healthy lifestyle?  Surely you can’t tell a likely candidate for this remediation program with just one look alone.

It goes deeper than that.  What if you, a healthcare representative, could only devote remedial care to a select few individuals given limited resources and time but you want to make sure that the whole remediation program is successful by achieving its intended goals?  Just imagine all that time, effort and resources spent only for the patient to relapse back into their old ways not too long after program completion- or even in the middle of the remediation process itself.

Deep Learning and Remediation

This is where deep learning comes in.  Also known as hierarchical learning or deep structured learning, Health IT Analytics defines deep learning as a type of machine learning that uses a layered algorithmic architecture to analyze data.  In deep learning models, data is filtered through a cascade of multiple layers, with each successive layer using the output from the previous one to inform its results.  Deep learning models can become more and more accurate as they process more data, essentially learning from previous results to refine their ability to make correlations and connections.

Deep learning models handle and process huge volumes of complex data through multi-layered analytics to provide fast, accurate, and actionable results or insights.  When applied to the scenario we mentioned beforehand, deep learning filters through that multitude of patient data and prioritizes those who need remedial care the most.

You can also align its findings to effectively identify individuals who will not only return monetary value to your healthcare brand, but at the same time are most likely to “engage” or participate in programs offered by your company, such as wellness, diet, fitness or exercise.  They can also be the best people to commit to avoiding poor lifestyle choices, such as overeating, smoking, and alcohol, helping guarantee the success of the remediation program.

With a combination of three decades of market research experience and conscientious use of AI, Cascade Strategies has been helping healthcare organizations develop advanced models to handle, filter and identify the likeliest of candidates for their program purposes.  Cascade Strategies helps industry professionals not only recognize their ideal customers but also reach out to them with some of the most effective and award-winning marketing campaigns, thanks to our array of services such as Brand Development Research and Segmentation Studies.  To see more examples of how we help leading worldwide companies achieve their goals, please visit our website.

Here are some of our suggestions for further reading on deep learning and healthcare:

https://builtin.com/artificial-intelligence/machine-learning-healthcare

https://research.aimultiple.com/deep-learning-in-healthcare/

https://healthitanalytics.com/features/types-of-deep-learning-their-uses-in-healthcare