Using data to help manage solutions

Almost every abuser of opioids starts their use because of a genuine medical need. Chronic pain due to a long term condition, or severe acute pain frequently due to a surgical procedure are the most frequent reasons, but the start is nearly always clinical.

What happens next is the critical issue. What are the triggers that lead patients to seek prescriptions from multiple physicians? We know that this leads to a buildup of their drug supplies which can lead to overdose, but any solutions to this crisis will need to include a good understanding of why this is occurring.

Complex patients

One thing we can be sure of is that these patients are not simple. They have co-morbidities that require treatment. This means they are taking other drugs, getting other procedures. And have complex treatment patterns. Many have conditions that were being treated before they had their first opioid, and many develop new conditions whilst they are receiving opioid therapy. How do these factors combine? Can they be assessed to identify patients at risk of abusing opioids before they do?

The answer is yes, these factors combine and yes they can be assessed – and most of the data required are readily available, but the analytics required are not straightforward.

Galileo analytics experience and expertise

At Galileo Analytics we have worked this kind of problem a number of times. Building the complex cohorts that are based on combinations of demographics, comorbidities, treatment patterns also requires the addition of sequencing. The way in which patients are treated changes due to either existing conditions or new conditions. Building cohorts based on diagnosis sequence, treatment flows, the presence or absence of certain procedures – all these factors come into play when trying to determine how they affect an outcome.

Whether trying to identify risk factors for opioid abuse or hypoglycemia, or any other outcome, the analysis requires the ability to combine all facets of the patient’s condition and treatment – and this means using longitudinal data like claims or EHR (or both).

We have extensive expertise and experience in dealing with these data sets to solve problems like this. If we can identify patients at risk of opioid abuse we can help identify treatment pathways that can head off the serious problem, and the same applies to all the different serious outcomes that we want to help prevent (as well as the good outcomes we want to encourage).

Simon Fitall

About Simon Fitall

Simon is a knowledge engineer with 30 years experience in market research, data analytics and business intelligence within the pharmaceutical, biotechnology and medical device industries. With multiple patents in the field of advanced medical data analysis, Simon is an expert in data analysis with more than 20 years experience in working with, analyzing and creating models with patient data.

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